diff --git a/.github/workflows/discovery.yml b/.github/workflows/discovery.yml
new file mode 100644
index 00000000..0298c382
--- /dev/null
+++ b/.github/workflows/discovery.yml
@@ -0,0 +1,46 @@
+name: EPDE Discovery (slow) tests
+
+on:
+  workflow_dispatch:
+
+jobs:
+  discovery:
+    runs-on: ubuntu-latest
+    timeout-minutes: 360
+
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+        operator: ["DeepXDEBasedFitness", "PIC", "L2LRFitness"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install torch
+          echo "PYTHONPATH=$PYTHONPATH:$(pwd)" >> $GITHUB_ENV
+
+      - name: Run discovery tests
+        run: |
+          pytest tests/functional/ -m functional \
+            --discovery --report \
+            --operators ${{ matrix.operator }} \
+            --ignore=tests/functional/tests/test_ns.py \
+            --timeout=3600
+
+      - name: Upload reports
+        uses: actions/upload-artifact@v4
+        if: always()
+        with:
+          name: reports-${{ matrix.operator }}
+          path: reports/
diff --git a/.github/workflows/test-functional.yml b/.github/workflows/test-functional.yml
new file mode 100644
index 00000000..0e3546a5
--- /dev/null
+++ b/.github/workflows/test-functional.yml
@@ -0,0 +1,47 @@
+name: EPDE Core Tests
+
+on:
+  workflow_dispatch:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    name: pytest (${{ matrix.python-version }}) / ${{ matrix.split }}
+    runs-on: ubuntu-latest
+    timeout-minutes: 30
+
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+        operator: ["L2LRFitness"]
+        split: [1, 2, 3, 4]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install torch
+          pip install pytest-xdist pytest-split pytest-timeout
+          echo "PYTHONPATH=$PYTHONPATH:$(pwd)" >> $GITHUB_ENV
+
+      - name: Run split tests
+        run: |
+          pytest tests/functional/ \
+            --splits 4 --group ${{ matrix.split }} \
+            --durations=20 \
+            --timeout=600 \
+            -m "not slow" \
+            --operators ${{ matrix.operator }}
diff --git a/.gitignore b/.gitignore
index 9904af17..35075865 100644
--- a/.gitignore
+++ b/.gitignore
@@ -50,6 +50,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+reports/
 
 # Translations
 *.mo
diff --git a/epde/integrate/deepxde_integration.py b/epde/integrate/deepxde_integration.py
index f1aa347b..c334fa6f 100644
--- a/epde/integrate/deepxde_integration.py
+++ b/epde/integrate/deepxde_integration.py
@@ -9,6 +9,8 @@
 import deepxde as dde
 from abc import ABC, abstractmethod
 
+os.makedirs(os.path.expanduser('~/.deepxde'), exist_ok=True)
+
 class SolverStrategy(ABC):
     @abstractmethod
     def solve(self, eq_list: List[Equation], var_names: List[str],
diff --git a/epde/operators/utils/parameters/default_parameters_multi_objective.json b/epde/operators/utils/parameters/default_parameters_multi_objective.json
index 6fa1c831..c513e429 100644
--- a/epde/operators/utils/parameters/default_parameters_multi_objective.json
+++ b/epde/operators/utils/parameters/default_parameters_multi_objective.json
@@ -30,10 +30,20 @@
         "penalty_coeff" : 0.2,
         "pinn_loss_mult" : 1e4
  	},
-	"DeepXDEBasedFitness" : {
-        "penalty_coeff" : 0.2,
-        "pinn_loss_mult" : 1e4
- 	},
+	"DeepXDEBasedFitness": {
+        "deepxde_config": {
+            "net": [95, 100, 95],
+            "activation": "tanh",
+            "optimizer": "adam",
+            "lr": 1e-3,
+            "num_domain": 1000,
+            "num_boundary": 200,
+            "num_initial": 200,
+            "epochs": 2000
+        },
+        "penalty_coeff": 0.2,
+        "error_metric": "rmse"
+    },
  	"ParetoLevelsCrossover" : {
 
  	},
diff --git a/projects/pic/data/ac/ac.py b/projects/pic/data/ac/ac.py
index aaad1e69..72e2d85c 100644
--- a/projects/pic/data/ac/ac.py
+++ b/projects/pic/data/ac/ac.py
@@ -78,15 +78,13 @@ def prepare_suboperators(fitness_operator: CompoundOperator, operator_params: di
     sparsity = LASSOSparsity()
     coeff_calc = LinRegBasedCoeffsEquation()
 
-    # sparsity = map_operator_between_levels(sparsity, 'gene level', 'chromosome level')
-    # coeff_calc = map_operator_between_levels(coeff_calc, 'gene level', 'chromosome level')
-
-    fitness_operator.set_suboperators({'sparsity': sparsity,
-                                       'coeff_calc': coeff_calc})
-    fitness_cond = lambda x: not getattr(x, 'fitness_calculated')
+    fitness_operator.set_suboperators({'sparsity': sparsity, 'coeff_calc': coeff_calc})
     fitness_operator.params = operator_params
-    fitness_operator = map_operator_between_levels(fitness_operator, 'gene level', 'chromosome level',
-                                                   objective_condition=fitness_cond)
+
+    if 'chromosome level' not in fitness_operator._tags:
+        fitness_cond = lambda x: not getattr(x, 'fitness_calculated')
+        fitness_operator = map_operator_between_levels(fitness_operator, 'gene level', 'chromosome level',
+                                                       objective_condition=fitness_cond)
     return fitness_operator
 
 def ac_data(filename: str):
@@ -98,6 +96,14 @@ def ac_data(filename: str):
     return grids, data
 
 
+def get_pic_network_summary(operator):
+    if operator.adapter is None or operator.adapter.net is None:
+        return None
+    net = operator.adapter.net
+    total_params = sum(p.numel() for p in net.parameters())
+    layers = [str(layer) for layer in net.layers] if hasattr(net, 'layers') else []
+    return {'total_parameters': total_params, 'layers': layers}
+
 def AC_test(operator: CompoundOperator, foldername: str, noise_level: int = 0):
     # Test scenario to evaluate performance on Allen-Cahn equation
     eq_ac_symbolic = '0.0001 * d^2u/dx1^2{power: 1.0} + -5.0 * u{power: 3.0} + 5.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
@@ -172,40 +178,45 @@ def ac_discovery(foldername, noise_level):
 if __name__ == "__main__":
     import torch
     from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+    global_var.solution_guess_nn = None
     print(torch.cuda.is_available())
     print(f"CUDA version linked with PyTorch: {torch.version.cuda}")
     # Operator = fitness.SolverBasedFitness # Replace by the developed PIC-based operator.
-    # Operator = fitness.PIC
-    #Operator = fitness.L2LRFitness
-    Operator = fitness.DeepXDEBasedFitness
+    #Operator = fitness.PIC
+    Operator = fitness.L2LRFitness
+    #Operator = fitness.DeepXDEBasedFitness
     params = EvolutionaryParams()
-    #operator_params = params.get_default_params_for_operator('DiscrepancyBasedFitnessWithCV') #{"penalty_coeff": 0.2, "pinn_loss_mult": 1e4}
-    try:
-        operator_params = params.get_default_params_for_operator('DeepXDEBasedFitness')
-    except Exception as e:
-        print(f"Предупреждение: не удалось загрузить параметры для DeepXDEBasedFitness: {e}")
-        print("Использую ручную конфигурацию.")
-        operator_params = {
-            "deepxde_config": {
-                "net": [50, 50, 50],
-                "activation": "tanh",
-                "optimizer": "adam",
-                "lr": 1e-3,
-                "num_domain": 1000,
-                "num_boundary": 200,
-                "num_initial": 200,
-                "iterations": 2
-            },
-            "penalty_coeff": 0.2,
-            "error_metric": "rmse"
-        }
+    operator_params = params.get_default_params_for_operator('DiscrepancyBasedFitnessWithCV') #{"penalty_coeff": 0.2, "pinn_loss_mult": 1e4}
+    #operator_params = params.get_default_params_for_operator('PIC')
+
+    # try:
+    #     operator_params = params.get_default_params_for_operator('DeepXDEBasedFitness')
+    # except Exception as e:
+    #     print(f"Предупреждение: не удалось загрузить параметры для DeepXDEBasedFitness: {e}")
+    #     print("Использую ручную конфигурацию.")
+    #     operator_params = {
+    #         "deepxde_config": {
+    #             "net": [50, 50, 50],
+    #             "activation": "tanh",
+    #             "optimizer": "adam",
+    #             "lr": 1e-3,
+    #             "num_domain": 1000,
+    #             "num_boundary": 200,
+    #             "num_initial": 200,
+    #             "iterations": 2
+    #         },
+    #         "penalty_coeff": 0.2,
+    #         "error_metric": "rmse"
+    #     }
 
     print('operator_params ', operator_params)
+
     fit_operator = prepare_suboperators(Operator(list(operator_params.keys())), operator_params)
+    #get_pic_network_summary(fit_operator)
 
     # Paths
     directory = os.path.dirname(os.path.realpath(__file__))
     ac_folder_name = os.path.join(directory)
 
-    #AC_test(fit_operator, ac_folder_name, 0)
-    ac_discovery(ac_folder_name, 0)
+    AC_test(fit_operator, ac_folder_name, 0)
+    # ac_discovery(ac_folder_name, 0)
diff --git a/projects/pic/data/burgers/burgers.py b/projects/pic/data/burgers/burgers.py
index 2943a3d5..f09c8176 100644
--- a/projects/pic/data/burgers/burgers.py
+++ b/projects/pic/data/burgers/burgers.py
@@ -49,6 +49,9 @@ def compare_equations(correct_symbolic: str, eq_incorrect_symbolic: str,
     for var in all_vars:
         correct_eq.vals[var].main_var_to_explain = var
         correct_eq.vals[var].metaparameters = metaparams
+        correct_eq.vals[var].weights_internal = np.ones(len(correct_eq.vals[var].structure) - 1)
+        correct_eq.vals[var].weights_internal_evald = True
+        correct_eq.vals[var].weights_final_evald = True
     print(correct_eq.text_form)
 
     incorrect_eq = translate_equation(eq_incorrect_symbolic, search_obj.pool,
@@ -56,6 +59,9 @@ def compare_equations(correct_symbolic: str, eq_incorrect_symbolic: str,
     for var in all_vars:
         incorrect_eq.vals[var].main_var_to_explain = var
         incorrect_eq.vals[var].metaparameters = metaparams
+        incorrect_eq.vals[var].weights_internal = np.ones(len(incorrect_eq.vals[var].structure) - 1)
+        incorrect_eq.vals[var].weights_internal_evald = True
+        incorrect_eq.vals[var].weights_final_evald = True
     print(incorrect_eq.text_form)
 
     fit_operator.apply(correct_eq, {})
@@ -109,8 +115,8 @@ def burgers_data(filename: str):
 
 def burgers_sindy_test(operator: CompoundOperator, foldername: str, noise_level: int = 0):
     # Test scenario to evaluate performance on Allen-Cahn equation
-    eq_burgers_symbolic = '0.0001 * d^2u/dx1^2{power: 1.0} + -5.0 * u{power: 3.0} + 5.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
-    eq_burgers_incorrect = '-1.0 * d^2u/dx0^2{power: 1.0} + 1.5 * u{power: 1.0} + -0.0 = du/dx0{power: 1.0}'
+    eq_burgers_symbolic = '-1.0 * u{power: 1.0} * du/dx1{power: 1.0} + 0.01 * d^2u/dx1^2{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
+    eq_burgers_incorrect = '0.02 * d^2u/dx1^2{power: 1.0} + -0.98 * u{power: 1.0} * du/dx1{power: 1.0} + 0.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
 
     grid, data = burgers_sindy_data(os.path.join(foldername, 'burgers.mat'))
     noised_data = noise_data(data, noise_level)
@@ -240,6 +246,6 @@ def burgers_sindy_discovery(foldername, noise_level):
     directory = os.path.dirname(os.path.realpath(__file__))
     burgers_folder_name = os.path.join(directory)
 
-    burgers_discovery(burgers_folder_name, 0)
+    # burgers_discovery(burgers_folder_name, 0)
     # burgers_sindy_test(fit_operator, burgers_folder_name, 0)
-    # burgers_sindy_discovery(burgers_folder_name, 0)
+    burgers_sindy_discovery(burgers_folder_name, 0)
diff --git a/projects/pic/data/kdv/kdv.py b/projects/pic/data/kdv/kdv.py
index f31e0dd1..fab95f0a 100644
--- a/projects/pic/data/kdv/kdv.py
+++ b/projects/pic/data/kdv/kdv.py
@@ -26,8 +26,8 @@
 
 def load_pretrained_PINN(ann_filename):
     try:
-        with open(ann_filename, 'rb') as data_input_file:
-            data_nn = pickle.load(data_input_file)
+        import torch
+        data_nn = torch.load(ann_filename, map_location=torch.device('cpu'))
     except FileNotFoundError:
         print('No model located, proceeding with ann approx. retraining.')
         data_nn = None
@@ -47,6 +47,9 @@ def compare_equations(correct_symbolic: str, eq_incorrect_symbolic: str,
     for var in all_vars:
         correct_eq.vals[var].main_var_to_explain = var
         correct_eq.vals[var].metaparameters = metaparams
+        correct_eq.vals[var].weights_internal = np.ones(len(correct_eq.vals[var].structure) - 1)
+        correct_eq.vals[var].weights_internal_evald = True
+        correct_eq.vals[var].weights_final_evald = True
     print(correct_eq.text_form)
 
     incorrect_eq = translate_equation(eq_incorrect_symbolic, search_obj.pool,
@@ -54,6 +57,9 @@ def compare_equations(correct_symbolic: str, eq_incorrect_symbolic: str,
     for var in all_vars:
         incorrect_eq.vals[var].main_var_to_explain = var
         incorrect_eq.vals[var].metaparameters = metaparams
+        incorrect_eq.vals[var].weights_internal = np.ones(len(correct_eq.vals[var].structure) - 1)
+        incorrect_eq.vals[var].weights_internal_evald = True
+        incorrect_eq.vals[var].weights_final_evald = True
     print(incorrect_eq.text_form)
 
     fit_operator.apply(correct_eq, {})
@@ -143,7 +149,7 @@ def KdV_test(operator: CompoundOperator, foldername: str, noise_level: int = 0):
     grid, data = kdv_data(os.path.join(foldername, 'data.csv'))
     # grid, data = kdv_data(os.path.join(foldername, 'Kdv.mat'))
     noised_data = noise_data(data, noise_level)
-    data_nn = load_pretrained_PINN(os.path.join(foldername, 'kdv_0_ann.pickle'))
+    data_nn = None #load_pretrained_PINN(os.path.join(foldername, 'kdv_0_ann.pickle'))
 
     print('Shapes:', data.shape, grid[0].shape)
     dimensionality = 1
@@ -153,7 +159,7 @@ def KdV_test(operator: CompoundOperator, foldername: str, noise_level: int = 0):
 
     epde_search_obj = EpdeSearch(use_solver = False, use_pic=True, boundary = 10,
                                  coordinate_tensors = (grid[0], grid[1]), verbose_params = {'show_iter_idx' : True},
-                                 device = 'cuda')
+                                 device = 'cuda' if torch.cuda.is_available() else 'cpu')
 
     custom_trigonometric_eval_fun = {
         'cos(t)sin(x)': lambda *grids, **kwargs: (np.cos(grids[0]) * np.sin(grids[1])) ** kwargs['power']}
@@ -200,7 +206,7 @@ def KdV_h_test(operator: CompoundOperator, foldername: str, noise_level: int = 0
 
     epde_search_obj = EpdeSearch(use_solver = False, use_pic=True, boundary = 20,
                                  coordinate_tensors = (grid[0], grid[1]), verbose_params = {'show_iter_idx' : True},
-                                 device = 'cuda')
+                                 device = 'cuda' if torch.cuda.is_available() else 'cpu')
 
     epde_search_obj.set_preprocessor(default_preprocessor_type='FD',
                                      preprocessor_kwargs={}) #'epochs_max': 5e4
@@ -232,7 +238,7 @@ def KdV_sga_test(operator: CompoundOperator, foldername: str, noise_level: int =
 
     epde_search_obj = EpdeSearch(use_solver = False, use_pic=True, boundary = 10,
                                  coordinate_tensors = (grid[0], grid[1]), verbose_params = {'show_iter_idx' : True},
-                                 device = 'cuda')
+                                 device = 'cuda' if torch.cuda.is_available() else 'cpu')
 
     epde_search_obj.set_preprocessor(default_preprocessor_type='FD',
                                      preprocessor_kwargs={}) #'epochs_max': 5e4
@@ -254,7 +260,7 @@ def kdv_discovery(foldername, noise_level):
 
     epde_search_obj = EpdeSearch(use_solver=False, use_pic=True,
                                       boundary=5,
-                                      coordinate_tensors=grid, device='cuda')
+                                      coordinate_tensors=grid, device='cuda' if torch.cuda.is_available() else 'cpu')
 
     # epde_search_obj.set_preprocessor(default_preprocessor_type='ANN',
     #                                     preprocessor_kwargs={'epochs_max' : 1e3})
@@ -307,7 +313,7 @@ def kdv_h_discovery(foldername, noise_level):
 
     epde_search_obj = EpdeSearch(use_solver=False, use_pic=True,
                                       boundary=20,
-                                      coordinate_tensors=grid, device='cuda')
+                                      coordinate_tensors=grid, device='cuda' if torch.cuda.is_available() else 'cpu')
 
     # epde_search_obj.set_preprocessor(default_preprocessor_type='ANN',
     #                                     preprocessor_kwargs={'epochs_max' : 1e3})
@@ -363,7 +369,7 @@ def kdv_sga_discovery(foldername, noise_level):
 
     epde_search_obj = EpdeSearch(use_solver=False, use_pic=True,
                                       boundary=20,
-                                      coordinate_tensors=grid, device='cuda')
+                                      coordinate_tensors=grid, device='cuda' if torch.cuda.is_available() else 'cpu')
 
     epde_search_obj.set_preprocessor(default_preprocessor_type='ANN',
                                         preprocessor_kwargs={'epochs_max' : 1e3})
@@ -421,7 +427,7 @@ def kdv_sindy_discovery(foldername, noise_level):
 
     epde_search_obj = EpdeSearch(use_solver=False, use_pic=True,
                                       boundary=(40, 100),
-                                      coordinate_tensors=grid, device='cuda')
+                                      coordinate_tensors=grid, device='cuda' if torch.cuda.is_available() else 'cpu')
 
     # epde_search_obj.set_preprocessor(default_preprocessor_type='ANN',
     #                                     preprocessor_kwargs={'epochs_max' : 1e3})
@@ -470,8 +476,8 @@ def kdv_sindy_discovery(foldername, noise_level):
     from epde.operators.utils.default_parameter_loader import EvolutionaryParams
     print("CUDA available:", torch.cuda.is_available())
     # Operator = fitness.SolverBasedFitness # Replace by the developed PIC-based operator.
-    # Operator = fitness.PIC
-    Operator = fitness.L2LRFitness
+    Operator = fitness.PIC
+    # Operator = fitness.L2LRFitness
     params = EvolutionaryParams()
     operator_params = params.get_default_params_for_operator('DiscrepancyBasedFitnessWithCV') #{"penalty_coeff": 0.2, "pinn_loss_mult": 1e4}
     # operator_params = {"penalty_coeff": 0.2, "pinn_loss_mult": 1e4}
@@ -482,11 +488,11 @@ def kdv_sindy_discovery(foldername, noise_level):
     directory = os.path.dirname(os.path.realpath(__file__))
     kdv_folder_name = os.path.join(directory)
 
-    # KdV_test(fit_operator, kdv_folder_name, 0)
+    KdV_test(fit_operator, kdv_folder_name, 0)
     # KdV_h_test(fit_operator, kdv_folder_name, 0)
     # KdV_sga_test(fit_operator, kdv_folder_name, 0)
 
     # kdv_discovery(kdv_folder_name, 0)
     # kdv_h_discovery(kdv_folder_name, 0)
     # kdv_sga_discovery(kdv_folder_name, 5)
-    kdv_sindy_discovery(kdv_folder_name, 0)
\ No newline at end of file
+    # kdv_sindy_discovery(kdv_folder_name, 0)
\ No newline at end of file
diff --git a/projects/pic/data/lorenz/lorenz.py b/projects/pic/data/lorenz/lorenz.py
index 0ff74cf6..c78e9c90 100644
--- a/projects/pic/data/lorenz/lorenz.py
+++ b/projects/pic/data/lorenz/lorenz.py
@@ -26,6 +26,19 @@
 
 import scipy.io as scio
 
+original_set_adapter = fitness.PIC.set_adapter
+def patched_set_adapter(self, net=None):
+    from epde.integrate import SolverAdapter
+    compiling_params = {'mode': 'autograd', 'tol':0.01, 'lambda_bound': 100}
+    optimizer_params = {}
+    training_params = {'epochs': 1e3, 'info_string_every': 1e3}
+    early_stopping_params = {'patience': 4, 'no_improvement_patience': 250}
+    self.adapter = SolverAdapter(net=net, use_cache=False, device='cpu')
+    self.adapter.set_compiling_params(**compiling_params)
+    self.adapter.set_optimizer_params(**optimizer_params)
+    self.adapter.set_early_stopping_params(**early_stopping_params)
+    self.adapter.set_training_params(**training_params)
+fitness.PIC.set_adapter = patched_set_adapter
 
 def load_pretrained_PINN(ann_filename):
     try:
@@ -74,10 +87,14 @@ def prepare_suboperators(fitness_operator: CompoundOperator, operator_params: di
     sparsity = LASSOSparsity()
     coeff_calc = LinRegBasedCoeffsEquation()
 
+    # Поднимаем подоператоры на уровень хромосомы для работы с SoEq
+    sparsity = map_operator_between_levels(sparsity, 'gene level', 'chromosome level')
+    coeff_calc = map_operator_between_levels(coeff_calc, 'gene level', 'chromosome level')
+
     fitness_operator.set_suboperators({'sparsity': sparsity, 'coeff_calc': coeff_calc})
     fitness_operator.params = operator_params
 
-    # Применяем маппинг только для операторов уровня 'gene level'
+    # Маппинг самого fitness_operator
     if 'chromosome level' not in fitness_operator._tags:
         fitness_cond = lambda x: not getattr(x, 'fitness_calculated')
         fitness_operator = map_operator_between_levels(fitness_operator, 'gene level', 'chromosome level',
@@ -156,14 +173,14 @@ def lorenz_test(fit_operator, noise_level=0):
     z = data[:end, 2]
 
     correct_eqs = [
-        '10.0 * v{power: 1.0} + -10.0 * u{power: 1.0} = du/dx0{power: 1.0}',
-        '28.0 * u{power: 1.0} + -1.0 * u{power: 1.0} * w{power: 1.0} + -1.0 * v{power: 1.0} = dv/dx0{power: 1.0}',
-        '1.0 * u{power: 1.0} * v{power: 1.0} + -2.6666666666666665 * w{power: 1.0} = dw/dx0{power: 1.0}'
+        '10.0 * v{power: 1.0} + -10.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}',
+        '28.0 * u{power: 1.0} + -1.0 * u{power: 1.0} * w{power: 1.0} + -1.0 * v{power: 1.0} + 0.0 = dv/dx0{power: 1.0}',
+        '1.0 * u{power: 1.0} * v{power: 1.0} + -2.6666666666666665 * w{power: 1.0} + 0.0 = dw/dx0{power: 1.0}'
     ]
     incorrect_eqs = [
-        '10.0 * v{power: 1.0} + -10.0 * u{power: 1.0} + 0.1 * u{power: 1.0} = du/dx0{power: 1.0}',
-        '28.0 * u{power: 1.0} + -1.0 * u{power: 1.0} * w{power: 1.0} + -1.0 * v{power: 1.0} + 0.1 * v{power: 1.0} = dv/dx0{power: 1.0}',
-        '1.0 * u{power: 1.0} * v{power: 1.0} + -2.6666666666666665 * w{power: 1.0} + 0.1 * w{power: 1.0} = dw/dx0{power: 1.0}'
+        '10.0 * v{power: 1.0} + -10.0 * u{power: 1.0} + 0.1 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}',
+        '28.0 * u{power: 1.0} + -1.0 * u{power: 1.0} * w{power: 1.0} + -1.0 * v{power: 1.0} + 0.1 * v{power: 1.0} + 0.0 = dv/dx0{power: 1.0}',
+        '1.0 * u{power: 1.0} * v{power: 1.0} + -2.6666666666666665 * w{power: 1.0} + 0.1 * w{power: 1.0} + 0.0 = dw/dx0{power: 1.0}'
     ]
 
     epde_search_obj = EpdeSearch(
@@ -226,16 +243,17 @@ def lorenz_discovery(noise_level):
 
     return epde_search_obj
 
-
 if __name__ == "__main__":
     import torch
     from epde.operators.utils.default_parameter_loader import EvolutionaryParams
     print(torch.cuda.is_available())
+    global_var.solution_guess_nn = None
     # Operator = fitness.SolverBasedFitness # Replace by the developed PIC-based operator.
     Operator = fitness.PIC
     #Operator = fitness.L2LRFitness
     params = EvolutionaryParams()
     operator_params = params.get_default_params_for_operator('PIC')#'DiscrepancyBasedFitnessWithCV') #{"penalty_coeff": 0.2, "pinn_loss_mult": 1e4}
+    #operator_params = params.get_default_params_for_operator('DiscrepancyBasedFitnessWithCV') #{"penalty_coeff": 0.2, "pinn_loss_mult": 1e4}
     # Operator = fitness.DeepXDEBasedFitness
     # params = EvolutionaryParams()
     #
@@ -261,5 +279,19 @@ def lorenz_discovery(noise_level):
     # print('operator_params ', operator_params)
     fit_operator = prepare_suboperators(Operator(list(operator_params.keys())), operator_params)
 
-    lorenz_discovery(0)
-    #lorenz_test(fit_operator, noise_level=0)
+    #lorenz_discovery(0)
+    lorenz_test(fit_operator, noise_level=0)
+
+
+    def get_pic_network_summary(operator):
+        if operator.adapter is None or operator.adapter.net is None:
+            return None
+        net = operator.adapter.net
+        total_params = sum(p.numel() for p in net.parameters())
+        layers = [str(layer) for layer in net.layers] if hasattr(net, 'layers') else []
+        return {'total_parameters': total_params, 'layers': layers}
+
+
+    pic_info = get_pic_network_summary(fit_operator)
+    print("PIC network summary:", pic_info)
+
diff --git a/projects/pic/data/lv/lv.py b/projects/pic/data/lv/lv.py
index 51d61687..fa0821d8 100644
--- a/projects/pic/data/lv/lv.py
+++ b/projects/pic/data/lv/lv.py
@@ -135,4 +135,4 @@ def lv_discovery(noise_level):
     print('operator_params ', operator_params)
     fit_operator = prepare_suboperators(Operator(list(operator_params.keys())), operator_params)
 
-    lv_discovery(0)
+    #lv_discovery(0)
diff --git a/projects/pic/data/ns/ns.py b/projects/pic/data/ns/ns.py
index 081fb59a..b0ea4643 100644
--- a/projects/pic/data/ns/ns.py
+++ b/projects/pic/data/ns/ns.py
@@ -7,6 +7,9 @@
 from typing import Tuple, List
 import numpy as np
 
+import copy
+from epde.interface.token_family import TFPool
+from epde.structure.main_structures import SoEq, Chromosome
 from epde.interface.prepared_tokens import CustomTokens, PhasedSine1DTokens, ConstantToken, CustomEvaluator
 from epde.interface.equation_translator import translate_equation
 from epde.interface.interface import EpdeSearch
@@ -71,6 +74,81 @@ def compare_equations(correct_symbolic: str, eq_incorrect_symbolic: str,
     return all([correct_eq.vals[var].coefficients_stability < incorrect_eq.vals[var].coefficients_stability for var in
                 all_vars])
 
+def create_equation_from_str(eq_str, target_var, base_pool, all_vars):
+    # Отладочная информация
+    print(f"\n[DEBUG] target_var = {target_var}, eq_str = {eq_str}")
+    print("[DEBUG] Families in pool:")
+    for fam in base_pool.families:
+        var_name = getattr(fam, 'variable', None)
+        tokens = getattr(fam, 'tokens', [])
+        print(f"  variable={var_name}, tokens={tokens}, demands_equation={fam.status.get('demands_equation', False)}")
+
+    # Сохраняем оригинальные состояния demands_equation для семейств других переменных
+    original_states = {}
+    for fam in base_pool.families:
+        if hasattr(fam, 'variable') and fam.variable is not None and fam.variable != target_var:
+            original_states[fam] = fam.status.get('demands_equation', False)
+            fam.status['demands_equation'] = False
+    try:
+        soeq = translate_equation(eq_str, base_pool, all_vars=[target_var])
+        eq = soeq.vals[target_var]
+    except Exception as e:
+        print(f"[DEBUG] Translation failed: {e}")
+        raise
+    finally:
+        for fam, state in original_states.items():
+            fam.status['demands_equation'] = state
+    return eq
+
+def compare_systems(correct_symbolic_list, incorrect_symbolic_list, search_obj, all_vars, fit_operator):
+    metaparams = {('sparsity', var): {'optimizable': False, 'value': 1E-6} for var in all_vars}
+
+    correct_eqs = {}
+    for var, eq_str in zip(all_vars, correct_symbolic_list):
+        eq = create_equation_from_str(eq_str, var, search_obj.pool, all_vars)
+        eq.main_var_to_explain = var
+        eq.metaparameters = metaparams
+        eq.weights_internal = np.ones(len(eq.structure) - 1)
+        eq.weights_internal_evald = True
+        eq.weights_final_evald = True
+        correct_eqs[var] = eq
+
+    correct_system = SoEq(search_obj.pool, metaparams)
+    correct_system.vals = Chromosome(correct_eqs, {})
+    correct_system.moeadd_set = True
+    print("Correct system:")
+    print(correct_system.text_form)
+
+    incorrect_eqs = {}
+    for var, eq_str in zip(all_vars, incorrect_symbolic_list):
+        eq = create_equation_from_str(eq_str, var, search_obj.pool, all_vars)
+        eq.main_var_to_explain = var
+        eq.metaparameters = metaparams
+        eq.weights_internal = np.ones(len(eq.structure) - 1)
+        eq.weights_internal_evald = True
+        eq.weights_final_evald = True
+        incorrect_eqs[var] = eq
+
+    incorrect_system = SoEq(search_obj.pool, metaparams)
+    incorrect_system.vals = Chromosome(incorrect_eqs, {})
+    incorrect_system.moeadd_set = True
+    print("Incorrect system:")
+    print(incorrect_system.text_form)
+
+    fit_operator.apply(correct_system, {})
+    fit_operator.apply(incorrect_system, {})
+
+    correct_stability = [correct_system.vals[var].coefficients_stability for var in all_vars]
+    incorrect_stability = [incorrect_system.vals[var].coefficients_stability for var in all_vars]
+    print("Correct stability:", correct_stability)
+    print("Incorrect stability:", incorrect_stability)
+
+    correct_fitness = [correct_system.vals[var].fitness_value for var in all_vars]
+    incorrect_fitness = [incorrect_system.vals[var].fitness_value for var in all_vars]
+    print("Correct fitness:", correct_fitness)
+    print("Incorrect fitness:", incorrect_fitness)
+
+    return all(cs < incs for cs, incs in zip(correct_stability, incorrect_stability))
 
 def prepare_suboperators(fitness_operator: CompoundOperator, operator_params: dict) -> CompoundOperator:
     sparsity = LASSOSparsity()
@@ -113,10 +191,21 @@ def ns_data(filename: str):
 
 
 def ns_test(operator: CompoundOperator, foldername: str, noise_level: int = 0):
-    # Test scenario to evaluate performance on Allen-Cahn equation
-    eq_ac_symbolic = '0.0001 * d^2u/dx1^2{power: 1.0} + -5.0 * u{power: 3.0} + 5.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
-    eq_ac_incorrect = '4.976781518840499 * u{power: 1.0} + 0.0001 * d^2u/dx1^2{power: 1.0} + -4.974425220166616 * u{power: 3.0} + 0.0 * du/dx1{power: 1.0} * d^2u/dx0^2{power: 1.0} + 0.002262543822130977 = du/dx0{power: 1.0}'
+    # Базовые строки для переменной u
+    eq_u_correct = '0.0001 * d^2u/dx1^2{power: 1.0} + -5.0 * u{power: 3.0} + 5.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
+    eq_u_incorrect = '4.976781518840499 * u{power: 1.0} + 0.0001 * d^2u/dx1^2{power: 1.0} + -4.974425220166616 * u{power: 3.0} + 0.0 * du/dx1{power: 1.0} * d^2u/dx0^2{power: 1.0} + 0.002262543822130977 = du/dx0{power: 1.0}'
+
+    # Для v и p – заменяем u на v/p и производные соответственно
+    def replace_var(s, old, new):
+        return s.replace(f'u', new).replace(f'du/dx0', f'd{new}/dx0').replace(f'd^2u/dx1^2', f'd^2{new}/dx1^2')
+
+    eq_v_correct = replace_var(eq_u_correct, 'u', 'v')
+    eq_v_incorrect = replace_var(eq_u_incorrect, 'u', 'v')
+    eq_p_correct = replace_var(eq_u_correct, 'u', 'p')
+    eq_p_incorrect = replace_var(eq_u_incorrect, 'u', 'p')
 
+    correct_system = [eq_u_correct, eq_v_correct, eq_p_correct]
+    incorrect_system = [eq_u_incorrect, eq_v_incorrect, eq_p_incorrect]
     grid, data = ns_data(os.path.join(foldername, 'cylinder_nektar_wake.mat'))
     # noised_data = noise_data(data, noise_level)
     # data_nn = load_pretrained_PINN(os.path.join(foldername, 'ac_ann_pretrained.pickle'))
@@ -131,10 +220,11 @@ def ns_test(operator: CompoundOperator, foldername: str, noise_level: int = 0):
     epde_search_obj.set_preprocessor(default_preprocessor_type='FD',
                                      preprocessor_kwargs={})
 
-    epde_search_obj.create_pool(data=data, variable_names=["u", "v", "p"], max_deriv_order=(1, 2, 2),
+    epde_search_obj.create_pool(data=data, variable_names=["u", "v", "p"], max_deriv_order=(2, 2, 2),
                                 additional_tokens=[])#, data_nn=data_nn
 
-    assert compare_equations([eq_ac_symbolic] * 3, [eq_ac_incorrect] * 3, epde_search_obj)
+    assert compare_systems(correct_system, incorrect_system, epde_search_obj, all_vars=['u', 'v', 'p'],
+                           fit_operator=fit_operator)
 
 
 def ns_discovery(foldername, noise_level):
@@ -199,5 +289,5 @@ def ns_discovery(foldername, noise_level):
     directory = os.path.dirname(os.path.realpath(__file__))
     ns_folder_name = os.path.join(directory)
 
-    # ns_test(fit_operator, ns_folder_name, 0)
-    ns_discovery(ns_folder_name, 0)
+    ns_test(fit_operator, ns_folder_name, 0)
+    # ns_discovery(ns_folder_name, 0)
diff --git a/projects/pic/data/wave/wave.py b/projects/pic/data/wave/wave.py
index 43d54785..501d3e12 100644
--- a/projects/pic/data/wave/wave.py
+++ b/projects/pic/data/wave/wave.py
@@ -47,7 +47,9 @@ def compare_equations(correct_symbolic: str, eq_incorrect_symbolic: str,
     for var in all_vars:
         correct_eq.vals[var].main_var_to_explain = var
         correct_eq.vals[var].metaparameters = metaparams
-        correct_eq.vals[var].simplified = True
+        correct_eq.vals[var].weights_internal = np.ones(len(correct_eq.vals[var].structure) - 1)
+        correct_eq.vals[var].weights_internal_evald = True
+        correct_eq.vals[var].weights_final_evald = True
     print(correct_eq.text_form)
 
     incorrect_eq = translate_equation(eq_incorrect_symbolic, search_obj.pool,
@@ -55,7 +57,9 @@ def compare_equations(correct_symbolic: str, eq_incorrect_symbolic: str,
     for var in all_vars:
         incorrect_eq.vals[var].main_var_to_explain = var
         incorrect_eq.vals[var].metaparameters = metaparams
-        incorrect_eq.vals[var].simplified = True
+        incorrect_eq.vals[var].weights_internal = np.ones(len(incorrect_eq.vals[var].structure) - 1)
+        incorrect_eq.vals[var].weights_internal_evald = True
+        incorrect_eq.vals[var].weights_final_evald = True
     print(incorrect_eq.text_form)
 
     fit_operator.apply(correct_eq, {})
@@ -193,5 +197,5 @@ def wave_discovery(foldername, noise_level):
     directory = os.path.dirname(os.path.realpath(__file__))
     wave_folder_name = os.path.join(directory)
 
-    # wave_test(fit_operator, wave_folder_name, 0)
-    wave_discovery(wave_folder_name, 0)
\ No newline at end of file
+    wave_test(fit_operator, wave_folder_name, 0)
+    # wave_discovery(wave_folder_name, 0)
\ No newline at end of file
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/__init__.py b/tests/functional/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/comparasion.py b/tests/functional/comparasion.py
new file mode 100644
index 00000000..b90484e3
--- /dev/null
+++ b/tests/functional/comparasion.py
@@ -0,0 +1,77 @@
+from abc import ABC, abstractmethod
+import numpy as np
+from epde.interface.equation_translator import translate_equation
+from epde.interface.token_family import TFPool
+from epde.structure.main_structures import SoEq, Chromosome
+import copy
+
+
+class ComparisonStrategy(ABC):
+    @abstractmethod
+    def build(self, symbolic, search_obj, all_vars):
+        pass
+
+    @abstractmethod
+    def compare(self, correct_obj, incorrect_obj, fit_operator, all_vars):
+        pass
+
+
+class SingleEquationComparison(ComparisonStrategy):
+    def build(self, symbolic, search_obj, all_vars):
+        metaparams = {("sparsity", var): {"optimizable": False, "value": 1e-6} for var in all_vars}
+        eq = translate_equation(symbolic, search_obj.pool, all_vars=all_vars)
+        for var in all_vars:
+            eq.vals[var].main_var_to_explain = var
+            eq.vals[var].metaparameters = metaparams
+            eq.vals[var].weights_internal = np.ones(len(eq.vals[var].structure) - 1)
+            eq.vals[var].weights_internal_evald = True
+            eq.vals[var].weights_final_evald = True
+
+            _, _, features = eq.vals[var].evaluate(normalize=False, return_val=False)
+            assert len(eq.vals[var].weights_final[:-1]) == (features.shape[1]), "Different number of features. Check the structure of the equation."
+
+        return eq
+
+    def compare(self, correct_obj, incorrect_obj, fit_operator, all_vars):
+        fit_operator.apply(correct_obj, {})
+        fit_operator.apply(incorrect_obj, {})
+        return all(
+            correct_obj.vals[var].coefficients_stability < incorrect_obj.vals[var].coefficients_stability
+            for var in all_vars
+        )
+
+
+class SystemComparison(ComparisonStrategy):
+    def _create_eq(self, eq_str, target_var, base_pool, all_vars):
+        families_copy = [copy.deepcopy(fam) for fam in base_pool.families]
+        for fam in families_copy:
+            if hasattr(fam, "variable") and fam.variable is not None and fam.variable != target_var:
+                fam.status["demands_equation"] = False
+        temp_pool = TFPool(families_copy)
+        soeq = translate_equation(eq_str, temp_pool, all_vars=[target_var])
+        return soeq.vals[target_var]
+
+    def build(self, symbolic_list, search_obj, all_vars):
+        metaparams = {("sparsity", var): {"optimizable": False, "value": 1e-6} for var in all_vars}
+        eqs = {}
+        for var, eq_str in zip(all_vars, symbolic_list):
+            eq = self._create_eq(eq_str, var, search_obj.pool, all_vars)
+            eq.main_var_to_explain = var
+            eq.metaparameters = metaparams
+            eq.weights_internal = np.ones(len(eq.structure) - 1)
+            eq.weights_internal_evald = True
+            eq.weights_final_evald = True
+            eqs[var] = eq
+
+        system = SoEq(search_obj.pool, metaparams)
+        system.vals = Chromosome(eqs, {})
+        system.moeadd_set = True
+        return system
+
+    def compare(self, correct_obj, incorrect_obj, fit_operator, all_vars):
+        fit_operator.apply(correct_obj, {})
+        fit_operator.apply(incorrect_obj, {})
+        return all(
+            correct_obj.vals[var].coefficients_stability < incorrect_obj.vals[var].coefficients_stability
+            for var in all_vars
+        )
\ No newline at end of file
diff --git a/tests/functional/conftest.py b/tests/functional/conftest.py
new file mode 100644
index 00000000..60cb0ba2
--- /dev/null
+++ b/tests/functional/conftest.py
@@ -0,0 +1,43 @@
+from pathlib import Path
+import pytest
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--discovery",
+        action="store_true",
+        default=False,
+        help="Run discovery mode instead of equation comparison",
+    )
+    parser.addoption(
+        "--report",
+        action="store_true",
+        default=False,
+        help="Save discovery report files",
+    )
+    parser.addoption(
+        "--report-dir",
+        action="store",
+        default="reports",
+        help="Base directory for reports",
+    )
+    parser.addoption(
+        "--operators",
+        action="store",
+        default="DeepXDEBasedFitness,PIC,L2LRFitness",
+        help="Comma-separated list of operators to test",
+    )
+
+@pytest.fixture
+def runtime_options(request):
+    operators = request.config.getoption("--operators")
+    operator_list = [op.strip() for op in operators.split(",") if op.strip()]
+    return {
+        "discovery": request.config.getoption("--discovery"),
+        "report": request.config.getoption("--report"),
+        "report_dir": Path(request.config.getoption("--report-dir")),
+        "operators": operator_list,
+    }
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "functional: functional tests")
+    config.addinivalue_line("markers", "discovery: discovery tests")
\ No newline at end of file
diff --git a/tests/functional/operator_factory.py b/tests/functional/operator_factory.py
new file mode 100644
index 00000000..cbbfb9bf
--- /dev/null
+++ b/tests/functional/operator_factory.py
@@ -0,0 +1,39 @@
+from epde.operators.common.coeff_calculation import LinRegBasedCoeffsEquation
+from epde.operators.common.sparsity import LASSOSparsity
+from epde.operators.utils.operator_mappers import map_operator_between_levels
+from epde.operators.utils.template import CompoundOperator
+import epde.operators.common.fitness as fitness
+
+class FitnessOperatorFactory:
+    @staticmethod
+    def create(name: str, params: dict) -> CompoundOperator:
+        cls_map = {
+            "PIC": fitness.PIC,
+            "DeepXDEBasedFitness": fitness.DeepXDEBasedFitness,
+            "L2LRFitness": fitness.L2LRFitness,
+        }
+        if name not in cls_map:
+            raise ValueError(f"Unknown operator: {name}")
+
+        operator = cls_map[name](list(params.keys()))
+        sparsity = LASSOSparsity()
+        coeff_calc = LinRegBasedCoeffsEquation()
+        if name == 'PIC':
+            sparsity = map_operator_between_levels(sparsity, 'gene level', 'chromosome level')
+            coeff_calc = map_operator_between_levels(coeff_calc, 'gene level', 'chromosome level')
+
+        operator.set_suboperators({
+            "sparsity": sparsity,
+            "coeff_calc": coeff_calc,
+        })
+        operator.params = params
+
+        if 'chromosome level' not in operator._tags:
+            fitness_cond = lambda x: not getattr(x, "fitness_calculated", False)
+            operator = map_operator_between_levels(
+                operator,
+                'gene level',
+                "chromosome level",
+                objective_condition=fitness_cond,
+            )
+        return operator
\ No newline at end of file
diff --git a/tests/functional/scenarios/__init__.py b/tests/functional/scenarios/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/ac/AC.mat b/tests/functional/scenarios/ac/AC.mat
new file mode 100644
index 00000000..cd18945f
Binary files /dev/null and b/tests/functional/scenarios/ac/AC.mat differ
diff --git a/tests/functional/scenarios/ac/__init__.py b/tests/functional/scenarios/ac/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/ac/ac.py b/tests/functional/scenarios/ac/ac.py
new file mode 100644
index 00000000..0b9a894e
--- /dev/null
+++ b/tests/functional/scenarios/ac/ac.py
@@ -0,0 +1,147 @@
+import os
+import json
+import pickle
+import torch
+import pytest
+from datetime import datetime
+
+import numpy as np
+from epde.interface.interface import EpdeSearch
+
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SingleEquationComparison
+from tests.functional.utils.timer import Timer
+
+
+class ACTest(EquationTestTemplate):
+    strategy = SingleEquationComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+    def all_vars(self):
+        return ["u"]
+
+    def correct_symbolic(self):
+        return '0.0001 * d^2u/dx1^2{power: 1.0} + -5.0 * u{power: 3.0} + 5.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
+
+    def incorrect_symbolic(self):
+        return ' 0.0001 * d^2u/dx1^2{power: 1.0} + -4.976781518840499 * u{power: 3.0} + 4.974425220166616 * u{power: 1.0} + 0.0 * du/dx1{power: 1.0} * d^2u/dx1^2{power: 1.0} + 0.002262543822130977 = du/dx0{power: 1.0}'
+
+    def load_data(self):
+        return np.load(os.path.join(self.foldername, "ac_data.npy"))
+
+    def load_pretrained_PINN(self):
+        ann_path = os.path.join(self.foldername, "ac_ann_pretrained.pickle")
+        try:
+            with open(ann_path, "rb") as f:
+                return pickle.load(f)
+        except FileNotFoundError:
+            print("No model located, proceeding without pretrained ANN.")
+            return None
+
+    @staticmethod
+    def noise_data(data, noise_level):
+        return noise_level * 0.01 * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def make_additional_tokens(self):
+        return []
+
+    def make_search(self):
+        grid, data = self.ac_data()
+        data_nn = self.load_pretrained_PINN()
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            use_pic=True,
+            boundary=(5, 12),
+            coordinate_tensors=(grid[0], grid[1]),
+            verbose_params={"show_iter_idx": True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type="FD", preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=data,
+            variable_names=["u"],
+            max_deriv_order=(2, 3),
+            additional_tokens=self.make_additional_tokens(),
+            data_nn=data_nn,
+        )
+        return epde_search_obj
+
+    def ac_data(self):
+        t = np.linspace(0.0, 1.0, 51)
+        x = np.linspace(-1.0, 0.984375, 128)
+        data = self.load_data()
+        grids = np.meshgrid(t, x, indexing="ij")
+        return grids, data
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "ac" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        _, data = self.ac_data()
+        noised_data = self.noise_data(data, self.noise_level)
+
+        search_obj.set_moeadd_params(population_size=16, training_epochs=1)
+
+        with Timer() as t:
+            search_obj.fit(
+                data=noised_data,
+                variable_names=["u"],
+                max_deriv_order=(2, 3),
+                derivs=None,
+                equation_terms_max_number=5,
+                data_fun_pow=3,
+                additional_tokens=self.make_additional_tokens(),
+                equation_factors_max_number={"factors_num": [1, 2], "probas": [0.65, 0.35]},
+                eq_sparsity_interval=(1e-12, 1e-0),
+                fourier_layers=False,
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+
+            report = {
+                "scenario": "AllenCahn",
+                "operator": operator_name,
+                "noise_level": self.noise_level,
+                "elapsed_sec": t.elapsed,
+            }
+            (report_dir / "summary.json").write_text(
+                json.dumps(report, ensure_ascii=False, indent=2),
+                encoding="utf-8",
+            )
+
+            raw_clusters = search_obj.equations(only_print=False, num=5)
+
+            def eq_to_text(eq):
+                return getattr(eq, "text_form", str(eq))
+
+            clusters_json = []
+            for idx, cluster in enumerate(raw_clusters):
+                equations_list = []
+                for eq in cluster:
+                    text = eq_to_text(eq)
+                    equations_list.append({"equation": text})
+                clusters_json.append(
+                    {
+                        "cluster_id": idx,
+                        "size": len(equations_list),
+                        "equations": equations_list,
+                    }
+                )
+
+            (report_dir / "equations.json").write_text(
+                json.dumps(clusters_json, ensure_ascii=False, indent=2),
+                encoding="utf-8",
+            )
+
+        return search_obj, t.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/ac/ac_ann_pretrained.pickle b/tests/functional/scenarios/ac/ac_ann_pretrained.pickle
new file mode 100644
index 00000000..5f35f05a
Binary files /dev/null and b/tests/functional/scenarios/ac/ac_ann_pretrained.pickle differ
diff --git a/tests/functional/scenarios/ac/ac_data.npy b/tests/functional/scenarios/ac/ac_data.npy
new file mode 100644
index 00000000..bbf0abbf
Binary files /dev/null and b/tests/functional/scenarios/ac/ac_data.npy differ
diff --git a/tests/functional/scenarios/burgers/__init__.py b/tests/functional/scenarios/burgers/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/burgers/burgers.mat b/tests/functional/scenarios/burgers/burgers.mat
new file mode 100644
index 00000000..7060b54f
Binary files /dev/null and b/tests/functional/scenarios/burgers/burgers.mat differ
diff --git a/tests/functional/scenarios/burgers/burgers.py b/tests/functional/scenarios/burgers/burgers.py
new file mode 100644
index 00000000..1cf553f5
--- /dev/null
+++ b/tests/functional/scenarios/burgers/burgers.py
@@ -0,0 +1,199 @@
+import os
+import json
+import pickle
+from datetime import datetime
+import torch
+import pytest
+
+import numpy as np
+import pandas as pd
+from scipy.io import loadmat
+
+from epde.interface.interface import EpdeSearch
+from epde import TrigonometricTokens, CacheStoredTokens
+
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SingleEquationComparison
+from tests.functional.utils.timer import Timer
+
+
+def load_pretrained_PINN(ann_filename):
+    try:
+        with open(ann_filename, "rb") as data_input_file:
+            data_nn = pickle.load(data_input_file)
+    except FileNotFoundError:
+        print("No model located, proceeding without pretrained ANN.")
+        data_nn = None
+    return data_nn
+
+
+class BurgersTest(EquationTestTemplate):
+    strategy = SingleEquationComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+    def all_vars(self):
+        return ["u"]
+
+    def correct_symbolic(self):
+        # Оставил в стиле вашего текущего сценария.
+        # Если это не истинная формула Burgers, замените на нужную.
+        return '-1.0 * u{power: 1.0} * du/dx1{power: 1.0} + 0.01 * d^2u/dx1^2{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
+
+    def incorrect_symbolic(self):
+        return '0.02 * d^2u/dx1^2{power: 1.0} + -0.98 * u{power: 1.0} * du/dx1{power: 1.0} + 0.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
+
+    @staticmethod
+    def noise_data(data, noise_level):
+        return noise_level * 0.01 * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "burgers" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    def _save_report(self, report_dir, operator_name, elapsed, search_obj):
+        report = {
+            "scenario": "Burgers",
+            "operator": operator_name,
+            "noise_level": self.noise_level,
+            "elapsed_sec": elapsed,
+        }
+        (report_dir / "summary.json").write_text(
+            json.dumps(report, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+        raw_clusters = search_obj.equations(only_print=False, num=5)
+
+        def eq_to_text(eq):
+            return getattr(eq, "text_form", str(eq))
+
+        clusters_json = []
+        for idx, cluster in enumerate(raw_clusters):
+            equations_list = []
+            for eq in cluster:
+                equations_list.append({"equation": eq_to_text(eq)})
+            clusters_json.append(
+                {
+                    "cluster_id": idx,
+                    "size": len(equations_list),
+                    "equations": equations_list,
+                }
+            )
+
+        (report_dir / "equations.json").write_text(
+            json.dumps(clusters_json, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def load_burgers_sindy_data(self, filename):
+        burg = loadmat(filename)
+        t = np.ravel(burg["t"])
+        x = np.ravel(burg["x"])
+        data = np.real(burg["usol"])
+        data = np.transpose(data)
+        grids = np.meshgrid(t, x, indexing="ij")
+        return grids, data
+
+    def load_burgers_csv_data(self, filename):
+        df = pd.read_csv(filename, header=None)
+        u = df.values
+        data = np.transpose(u)
+        t = np.linspace(0, 1, 101)
+        x = np.linspace(-1000, 0, 101)
+        grids = np.meshgrid(t, x, indexing="ij")
+        return grids, data
+
+    def make_additional_tokens_sindy(self):
+        return []
+
+    def make_additional_tokens_discovery(self, grid):
+        custom_grid_tokens = CacheStoredTokens(
+            token_type="grid",
+            token_labels=["t", "x"],
+            token_tensors={"t": grid[0], "x": grid[1]},
+            params_ranges={"power": (1, 1)},
+            params_equality_ranges=None,
+        )
+        trig_tokens = TrigonometricTokens(dimensionality=dimensionality, freq=(0.999, 1.001))
+        return [custom_grid_tokens]
+
+    def make_search(self):
+        grid, data = self.load_burgers_sindy_data(os.path.join(self.foldername, "burgers.mat"))
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            use_pic=True,
+            boundary=10,
+            coordinate_tensors=(grid[0], grid[1]),
+            verbose_params={"show_iter_idx": True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type="FD", preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=data,
+            variable_names=["u"],
+            max_deriv_order=(2, 2),
+            additional_tokens=self.make_additional_tokens_sindy(),
+        )
+        return epde_search_obj
+
+    @pytest.mark.slow
+    def run_sindy_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        grid, data = self.load_burgers_sindy_data(os.path.join(self.foldername, "burgers.mat"))
+        noised_data = self.noise_data(data, self.noise_level)
+
+        search_obj.set_moeadd_params(population_size=16, training_epochs=15)
+
+        with Timer() as t:
+            search_obj.fit(
+                data=noised_data,
+                variable_names=["u"],
+                max_deriv_order=(2, 3),
+                derivs=None,
+                equation_terms_max_number=5,
+                data_fun_pow=3,
+                additional_tokens=[],
+                equation_factors_max_number={"factors_num": [1, 2], "probas": [0.65, 0.35]},
+                eq_sparsity_interval=(1e-5, 1e2),
+                fourier_layers=False,
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, t.elapsed, search_obj)
+
+        return search_obj, t.elapsed
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        grid, data = self.load_burgers_csv_data(os.path.join(self.foldername, "burgers_sln_100.csv"))
+        noised_data = self.noise_data(data, self.noise_level)
+
+        search_obj.set_moeadd_params(population_size=16, training_epochs=2)
+
+        with Timer() as t:
+            search_obj.fit(
+                data=noised_data,
+                variable_names=["u"],
+                max_deriv_order=(2, 3),
+                derivs=None,
+                equation_terms_max_number=5,
+                data_fun_pow=3,
+                additional_tokens=self.make_additional_tokens_discovery(grid),
+                equation_factors_max_number={"factors_num": [1, 2], "probas": [0.65, 0.35]},
+                eq_sparsity_interval=(1e-5, 1e2),
+                fourier_layers=False,
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, t.elapsed, search_obj)
+
+        return search_obj, t.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/kdv/__init__.py b/tests/functional/scenarios/kdv/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/kdv/data.csv b/tests/functional/scenarios/kdv/data.csv
new file mode 100644
index 00000000..8ae7959d
--- /dev/null
+++ b/tests/functional/scenarios/kdv/data.csv
@@ -0,0 +1,81 @@
+0.,-0.0019739670361023504,-0.006482645226773692,-0.011103392763830326,-0.015700143444398944,-0.020235802943216728,-0.02470395778230241,-0.02908901432257807,-0.03337537892496579,-0.037547630513554545,-0.04159453933875758,-0.045507815908937335,-0.04927920397573234,-0.05290044729078114,-0.05636348686561739,-0.059664981522786986,-0.06280622683491628,-0.06578869461806558,-0.06861385668829521,-0.07128318486166546,-0.07379815095423667,-0.07616127837011323,-0.07837848856315749,-0.08045548051635663,-0.08239791319189388,-0.08421144555195238,-0.08590173655871532,-0.08747444517436587,-0.08893523036108718,-0.09028981269053701,-0.09154353377389204,-0.09270123247737576,-0.09376773786557728,-0.09474787900308573,-0.09564648495449028,-0.09646838478438005,-0.09721840755734416,-0.09790092667096612,-0.09851884711905769,-0.09907517369741872,-0.09957292859520192,-0.10001513400156002,-0.10040481210564577,-0.10074498509661187,-0.10103867516361106,-0.10128851304740741,-0.1014960077630678,-0.101662872440963,-0.10179083695609314,-0.10188163118345839,-0.1019369849980589,-0.10195862827489481,-0.10194829088896627,-0.10190745563427599,-0.10183693429131115,-0.10173769883724228,-0.1016107322959189,-0.10145701769119056,-0.1012775380469068,-0.10107327638691715,-0.10084521573507116,-0.10059419245983903,-0.10032066112805348,-0.10002518535297272,-0.09970833551722458,-0.09937068200343686,-0.09901279519423736,-0.09863524547225389,-0.09823860322011427,-0.09782334909376511,-0.09738974000378742,-0.09693810792105838,-0.09646878908513885,-0.09598211973558969,-0.09547843611197177,-0.09495807445384592,-0.09442137100077302,-0.09386860184491955,-0.09329991110711766,-0.09271550848552544,-0.09211560677215941
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diff --git a/tests/functional/scenarios/kdv/kdv.py b/tests/functional/scenarios/kdv/kdv.py
new file mode 100644
index 00000000..e7387df0
--- /dev/null
+++ b/tests/functional/scenarios/kdv/kdv.py
@@ -0,0 +1,213 @@
+import os
+import json
+from datetime import datetime
+import scipy.io as scio
+
+import numpy as np
+import torch
+import pytest
+from epde.interface.prepared_tokens import CustomTokens, CustomEvaluator
+from epde.interface.interface import EpdeSearch
+from epde import TrigonometricTokens
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SingleEquationComparison
+from tests.functional.utils.timer import Timer
+
+
+class KdVTest(EquationTestTemplate):
+    strategy = SingleEquationComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+    def all_vars(self):
+        return ["u"]
+
+    def correct_symbolic(self):
+        return '-6.0 * du/dx1{power: 1.0} * u{power: 1.0} + -1.0 * d^3u/dx1^3{power: 1.0} + \
+                           1.0 * cos(t)sin(x){power: 1.0} + \
+                           0.0 = du/dx0{power: 1.0}'
+
+    def incorrect_symbolic(self):
+        return '0.04 * d^2u/dx1^2{power: 1} + 0. = d^2u/dx0^2{power: 1}'
+
+    def load_data(self):
+        filename = os.path.join(self.foldername, "data.csv")
+        data = np.loadtxt(filename, delimiter=",").T
+        shape = 80
+        t = np.linspace(0, 1, shape + 1)
+        x = np.linspace(0, 1, shape + 1)
+        grids = np.meshgrid(t, x, indexing="ij")
+        return grids, data
+
+    def load_kdv_sindy_data(self):
+        filename = os.path.join(self.foldername, "kdv_sindy.mat")
+        data = scio.loadmat(filename)
+        t = np.ravel(data["t"])
+        x = np.ravel(data["x"])
+        u = np.real(data["usol"])
+        u = np.transpose(u)
+        grids = np.meshgrid(t, x, indexing="ij")
+        return grids, u
+
+    @staticmethod
+    def noise_data(data, noise_level):
+        return noise_level * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "kdv" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    def make_custom_tokens(self):
+        custom_trigonometric_eval_fun = {
+            "cos(t)sin(x)": lambda *grids, **kwargs: (
+                np.cos(grids[0]) * np.sin(grids[1])
+            ) ** kwargs["power"]
+        }
+        custom_trig_evaluator = CustomEvaluator(
+            custom_trigonometric_eval_fun,
+            eval_fun_params_labels=["power"],
+        )
+        return CustomTokens(
+            token_type="trigonometric",
+            token_labels=["cos(t)sin(x)"],
+            evaluator=custom_trig_evaluator,
+            params_ranges={"power": (1, 1)},
+            params_equality_ranges={},
+            meaningful=True,
+            unique_token_type=True,
+        )
+
+    def _save_report(self, report_dir, operator_name, elapsed, search_obj):
+        report = {
+            "scenario": "KdV",
+            "operator": operator_name,
+            "noise_level": self.noise_level,
+            "elapsed_sec": elapsed,
+        }
+        (report_dir / "summary.json").write_text(
+            json.dumps(report, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+        raw_clusters = search_obj.equations(only_print=False, num=5)
+
+        def eq_to_text(eq):
+            return getattr(eq, "text_form", str(eq))
+
+        clusters_json = []
+        for idx, cluster in enumerate(raw_clusters):
+            equations_list = []
+            for eq in cluster:
+                equations_list.append({"equation": eq_to_text(eq)})
+            clusters_json.append(
+                {
+                    "cluster_id": idx,
+                    "size": len(equations_list),
+                    "equations": equations_list,
+                }
+            )
+
+        (report_dir / "equations.json").write_text(
+            json.dumps(clusters_json, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def make_search(self):
+        grid, data = self.load_data()
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            use_pic=True,
+            boundary=10,
+            coordinate_tensors=(grid[0], grid[1]),
+            verbose_params={"show_iter_idx": True},
+            device="cuda" if torch.cuda.is_available() else "cpu",
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type="FD", preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=data,
+            variable_names=["u"],
+            max_deriv_order=(2, 3),
+            additional_tokens=[self.make_custom_tokens()],
+        )
+        return epde_search_obj
+
+    def make_search_sindy(self):
+        grid, data = self.load_kdv_sindy_data()
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            use_pic=True,
+            boundary=(40, 100),
+            coordinate_tensors=grid,
+            verbose_params={"show_iter_idx": True},
+            device="cuda" if torch.cuda.is_available() else "cpu",
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type="poly", preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=data,
+            variable_names=["u"],
+            max_deriv_order=(2, 3),
+            additional_tokens=[],
+        )
+        return epde_search_obj
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        _, data = self.load_data()
+        noised_data = self.noise_data(data, self.noise_level)
+
+        search_obj.set_moeadd_params(population_size=16, training_epochs=5)
+
+        with Timer() as t:
+            search_obj.fit(
+                data=noised_data,
+                variable_names=["u"],
+                max_deriv_order=(2, 3),
+                derivs=None,
+                equation_terms_max_number=10,
+                data_fun_pow=3,
+                additional_tokens=[self.make_custom_tokens()],
+                equation_factors_max_number={"factors_num": [1, 2], "probas": [0.65, 0.35]},
+                eq_sparsity_interval=(1e-5, 1e-2),
+                fourier_layers=False,
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, t.elapsed, search_obj)
+
+        return search_obj, t.elapsed
+
+    @pytest.mark.slow
+    def run_sindy_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        grid, data = self.load_kdv_sindy_data()
+        noised_data = self.noise_data(data, self.noise_level)
+
+        search_obj.set_moeadd_params(population_size=16, training_epochs=1)
+
+        with Timer() as t:
+            search_obj.fit(
+                data=noised_data,
+                variable_names=["u"],
+                max_deriv_order=(2, 3),
+                derivs=None,
+                equation_terms_max_number=5,
+                data_fun_pow=3,
+                additional_tokens=[],
+                equation_factors_max_number={"factors_num": [1, 2], "probas": [0.65, 0.35]},
+                eq_sparsity_interval=(1e-12, 1e-0),
+                fourier_layers=False,
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name + "_sindy")
+            self._save_report(report_dir, operator_name + "_sindy", t.elapsed, search_obj)
+
+        return search_obj, t.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/kdv/kdv_sindy.mat b/tests/functional/scenarios/kdv/kdv_sindy.mat
new file mode 100644
index 00000000..3d9cd081
Binary files /dev/null and b/tests/functional/scenarios/kdv/kdv_sindy.mat differ
diff --git a/tests/functional/scenarios/ks/__init__.py b/tests/functional/scenarios/ks/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/ks/ks.py b/tests/functional/scenarios/ks/ks.py
new file mode 100644
index 00000000..e11a7d6c
--- /dev/null
+++ b/tests/functional/scenarios/ks/ks.py
@@ -0,0 +1,156 @@
+import os
+import json
+from datetime import datetime
+from typing import List
+import pytest
+import numpy as np
+import scipy.io as scio
+import torch
+from epde.interface.prepared_tokens import CustomTokens, CustomEvaluator
+from epde.interface.interface import EpdeSearch
+from epde import TrigonometricTokens, CacheStoredTokens
+
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SingleEquationComparison
+from tests.functional.utils.timer import Timer
+
+
+class KSTest(EquationTestTemplate):
+    strategy = SingleEquationComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+    def all_vars(self):
+        return ["u"]
+
+    def correct_symbolic(self):
+        return "-1.0 * d^2u/dx1^2{power: 1.0} + -1.0 * d^4u/dx1^4{power: 1.0} + -1.0 * u{power: 1.0} * du/dx1{power: 1.0} + 0.0 = du/dx0{power: 1.0}"
+
+    def incorrect_symbolic(self):
+        return "-1.0 * d^2u/dx1^2{power: 1.0} + 1.5 * u{power: 1.0} + -0.0 = du/dx0{power: 1.0}"
+
+    @staticmethod
+    def noise_data(data, noise_level):
+        return noise_level * 0.01 * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def load_ks_data(self, filename):
+        data = scio.loadmat(filename)
+        t = np.ravel(data["tt"])
+        x = np.ravel(data["x"])
+        u = data["uu"].T
+        grids = np.meshgrid(t, x, indexing="ij")
+        return grids, u
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "ks" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    def _save_report(self, report_dir, operator_name, elapsed, search_obj):
+        report = {
+            "scenario": "KS",
+            "operator": operator_name,
+            "noise_level": self.noise_level,
+            "elapsed_sec": elapsed,
+        }
+        (report_dir / "summary.json").write_text(
+            json.dumps(report, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+        raw_clusters = search_obj.equations(only_print=False, num=5)
+
+        def eq_to_text(eq):
+            return getattr(eq, "text_form", str(eq))
+
+        clusters_json = []
+        for idx, cluster in enumerate(raw_clusters):
+            equations_list = []
+            for eq in cluster:
+                equations_list.append({"equation": eq_to_text(eq)})
+            clusters_json.append(
+                {
+                    "cluster_id": idx,
+                    "size": len(equations_list),
+                    "equations": equations_list,
+                }
+            )
+
+        (report_dir / "equations.json").write_text(
+            json.dumps(clusters_json, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def make_search(self):
+        grid, data = self.load_ks_data(os.path.join(self.foldername, "kuramoto_sivishinky.mat"))
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            use_pic=True,
+            boundary=(50, 400),
+            coordinate_tensors=grid,
+            verbose_params={"show_iter_idx": True},
+            device="cuda" if torch.cuda.is_available() else "cpu",
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type="FD", preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=data,
+            variable_names=["u"],
+            max_deriv_order=(1, 4),
+            additional_tokens=[],
+        )
+        return epde_search_obj
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        grid, data = self.load_ks_data(os.path.join(self.foldername, "kuramoto_sivishinky.mat"))
+        noised_data = self.noise_data(data, self.noise_level)
+
+        search_obj.set_moeadd_params(population_size=16, training_epochs=3)
+
+        custom_grid_tokens = CacheStoredTokens(
+            token_type="grid",
+            token_labels=["t", "x"],
+            token_tensors={"t": grid[0], "x": grid[1]},
+            params_ranges={"power": (1, 1)},
+            params_equality_ranges=None,
+        )
+
+        custom_trigonometric_eval_fun = {
+            "cos(t)sin(x)": lambda *grids, **kwargs: (np.cos(grids[0]) * np.sin(grids[1])) ** kwargs["power"]
+        }
+        custom_trig_evaluator = CustomEvaluator(custom_trigonometric_eval_fun, eval_fun_params_labels=["power"])
+        custom_trig_tokens = CustomTokens(
+            token_type="trigonometric",
+            token_labels=["cos(t)sin(x)"],
+            evaluator=custom_trig_evaluator,
+            params_ranges={"power": (1, 1)},
+            params_equality_ranges={},
+            meaningful=True,
+            unique_token_type=True,
+        )
+
+        with Timer() as t:
+            search_obj.fit(
+                data=noised_data,
+                variable_names=["u"],
+                max_deriv_order=(1, 4),
+                derivs=None,
+                equation_terms_max_number=7,
+                data_fun_pow=1,
+                additional_tokens=[custom_grid_tokens, custom_trig_tokens],
+                equation_factors_max_number={"factors_num": [1, 2], "probas": [0.65, 0.35]},
+                eq_sparsity_interval=(1e-12, 1e-0),
+                fourier_layers=False,
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, t.elapsed, search_obj)
+
+        return search_obj, t.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/ks/kuramoto_sivishinky.mat b/tests/functional/scenarios/ks/kuramoto_sivishinky.mat
new file mode 100644
index 00000000..bb2ed7e1
Binary files /dev/null and b/tests/functional/scenarios/ks/kuramoto_sivishinky.mat differ
diff --git a/tests/functional/scenarios/lorenz/__init__.py b/tests/functional/scenarios/lorenz/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/lorenz/lorenz.npy b/tests/functional/scenarios/lorenz/lorenz.npy
new file mode 100644
index 00000000..8fea5966
Binary files /dev/null and b/tests/functional/scenarios/lorenz/lorenz.npy differ
diff --git a/tests/functional/scenarios/lorenz/lorenz.py b/tests/functional/scenarios/lorenz/lorenz.py
new file mode 100644
index 00000000..b41df3ed
--- /dev/null
+++ b/tests/functional/scenarios/lorenz/lorenz.py
@@ -0,0 +1,170 @@
+import os
+import json
+import pickle
+import torch
+from datetime import datetime
+import pytest
+import numpy as np
+from epde.interface.interface import EpdeSearch
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SystemComparison
+from epde import TrigonometricTokens, GridTokens
+from tests.functional.utils.timer import Timer
+
+class LorenzTest(EquationTestTemplate):
+    strategy = SystemComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+        self.dimensionality = None
+
+    def all_vars(self):
+        return ["u", "v", "w"]
+
+    def correct_symbolic(self):
+        return [
+        '10.0 * v{power: 1.0} + -10.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}',
+        '28.0 * u{power: 1.0} + -1.0 * u{power: 1.0} * w{power: 1.0} + -1.0 * v{power: 1.0} + 0.0 = dv/dx0{power: 1.0}',
+        '1.0 * u{power: 1.0} * v{power: 1.0} + -2.6666666666666665 * w{power: 1.0} + 0.0 = dw/dx0{power: 1.0}'
+    ]
+
+    def incorrect_symbolic(self):
+        return [
+        '10.0 * v{power: 1.0} + -10.0 * u{power: 1.0} + 0.1 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}',
+        '28.0 * u{power: 1.0} + -1.0 * u{power: 1.0} * w{power: 1.0} + -1.0 * v{power: 1.0} + 0.1 * v{power: 1.0} + 0.0 = dv/dx0{power: 1.0}',
+        '1.0 * u{power: 1.0} * v{power: 1.0} + -2.6666666666666665 * w{power: 1.0} + 0.1 * w{power: 1.0} + 0.0 = dw/dx0{power: 1.0}'
+    ]
+
+    def load_pretrained_PINN(self):
+        lorenz_path = os.path.join(self.foldername, "lorenz_pretrained.pickle")
+        try:
+            with open(lorenz_path, 'rb') as f:
+                return pickle.load(f)
+        except FileNotFoundError:
+            print('No model located, proceeding with ann approx. retraining.')
+            return None
+
+    def make_additional_tokens(self):
+        return []
+
+    @staticmethod
+    def noise_data(data, noise_level):
+        return noise_level * 0.01 * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def make_additional_tokens(self):
+        trig_tokens = TrigonometricTokens(freq=(2 - 1e-8, 2 + 1e-8), dimensionality=self.dimensionality)
+        grid_tokens = GridTokens(["x_0"], dimensionality=self.dimensionality, max_power=2)
+        return [trig_tokens]
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "lorenz" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    def _save_report(self, report_dir, operator_name, elapsed, search_obj):
+        report = {
+            "scenario": "Lorenz",
+            "operator": operator_name,
+            "noise_level": self.noise_level,
+            "elapsed_sec": elapsed,
+        }
+        (report_dir / "summary.json").write_text(
+            json.dumps(report, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+        raw_clusters = search_obj.equations(only_print=False, num=1)
+
+        def eq_to_text(eq):
+            return getattr(eq, "text_form", str(eq))
+
+        clusters_json = []
+        for idx, cluster in enumerate(raw_clusters):
+            equations_list = []
+            for eq in cluster:
+                equations_list.append({"equation": eq_to_text(eq)})
+            clusters_json.append(
+                {
+                    "cluster_id": idx,
+                    "size": len(equations_list),
+                    "equations": equations_list,
+                }
+            )
+
+        (report_dir / "equations.json").write_text(
+            json.dumps(clusters_json, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def make_search(self):
+        t, data = self.lorenz_data()
+        end = 1000
+        t = t[:end]
+        x = data[:end, 0]
+        y = data[:end, 1]
+        z = data[:end, 2]
+
+        self.dimensionality = x.ndim - 1
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            multiobjective_mode=True,
+            use_pic=True,
+            boundary=(100,),
+            coordinate_tensors=[t],
+            verbose_params={'show_iter_idx': True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=[x, y, z],
+            variable_names=['u', 'v', 'w'],
+            max_deriv_order=1,
+            additional_tokens=[],
+        )
+        return epde_search_obj
+
+    def lorenz_data(self):
+        t = np.load(os.path.join(os.path.dirname(__file__), 't.npy'))
+        data = np.load(os.path.join(os.path.dirname(__file__), 'lorenz.npy'))
+        return t, data
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        t, data = self.lorenz_data()
+        noised_data = self.noise_data(data, self.noise_level)
+        end = 1000
+        t = t[:end]
+        x = noised_data[:end, 0]
+        y = noised_data[:end, 1]
+        z = noised_data[:end, 2]
+
+        epde_search_obj = EpdeSearch(use_solver=False, multiobjective_mode=True, use_pic=True, boundary=(100),
+                                     coordinate_tensors=[t, ], verbose_params={'show_iter_idx': True},
+                                     device='cuda' if torch.cuda.is_available() else 'cpu')
+
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD',
+                                         preprocessor_kwargs={})
+
+        popsize = 16
+        epde_search_obj.set_moeadd_params(population_size=popsize, training_epochs=5)
+
+        factors_max_number = {'factors_num': [1, 2], 'probas': [0.8, 0.2]}
+
+        with Timer() as tim:
+            epde_search_obj.fit(data=[x, y, z], variable_names=['u', 'v', 'w'], max_deriv_order=(1,),
+                                equation_terms_max_number=5, data_fun_pow=1, additional_tokens= self.make_additional_tokens(),
+                                equation_factors_max_number=factors_max_number,
+                                eq_sparsity_interval=(1e-8, 1e-0))
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, tim.elapsed, epde_search_obj)
+
+        return search_obj, tim.elapsed
+
diff --git a/tests/functional/scenarios/lorenz/t.npy b/tests/functional/scenarios/lorenz/t.npy
new file mode 100644
index 00000000..6e33a553
Binary files /dev/null and b/tests/functional/scenarios/lorenz/t.npy differ
diff --git a/tests/functional/scenarios/lv/__init__.py b/tests/functional/scenarios/lv/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/lv/data_20.npy b/tests/functional/scenarios/lv/data_20.npy
new file mode 100644
index 00000000..2e882cc8
Binary files /dev/null and b/tests/functional/scenarios/lv/data_20.npy differ
diff --git a/tests/functional/scenarios/lv/lv.py b/tests/functional/scenarios/lv/lv.py
new file mode 100644
index 00000000..4dd4c7d4
--- /dev/null
+++ b/tests/functional/scenarios/lv/lv.py
@@ -0,0 +1,159 @@
+import os
+import json
+import numpy as np
+import torch
+from datetime import datetime
+import pytest
+from epde.interface.interface import EpdeSearch
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SystemComparison
+from epde import TrigonometricTokens, GridTokens
+from tests.functional.utils.timer import Timer
+
+class LotkaVolterraTest(EquationTestTemplate):
+    strategy = SystemComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+        self.dimensionality = 0   # только время
+
+    def all_vars(self):
+        return ["u", "v"]
+
+    def correct_symbolic(self):
+        return [
+            '0.6666666666666666 * u{power: 1.0} + -1.3333333333333333 * u{power: 1.0} * v{power: 1.0} + 0.0 = du/dx0{power: 1.0}',
+            '1.0 * u{power: 1.0} * v{power: 1.0} + -1.0 * v{power: 1.0} + 0.0 = dv/dx0{power: 1.0}'
+        ]
+
+    def incorrect_symbolic(self):
+        return [
+            '0.66 * u{power: 1.0} + -1.33 * u{power: 1.0} * v{power: 1.0} + 0.0 * u{power: 2.0} + 0.001 = du/dx0{power: 1.0}',
+            '0.99 * u{power: 1.0} * v{power: 1.0} + -0.99 * v{power: 1.0} + 0.0 * v{power: 2.0} + 0.001 = dv/dx0{power: 1.0}'
+        ]
+
+    def lv_data(self):
+        """Загружает временную сетку и данные (x, y) из .npy файлов."""
+        t = np.load(os.path.join(self.foldername, 't_20.npy'))   # путь к файлу времени
+        data = np.load(os.path.join(self.foldername, 'data_20.npy'))  # два столбца: u, v
+        return t, data
+
+    def make_additional_tokens(self):
+        # Для LV можно использовать тригонометрические и сеточные токены, как в lv_discovery
+        trig_tokens = TrigonometricTokens(freq=(2 - 1e-8, 2 + 1e-8), dimensionality=self.dimensionality)
+        grid_tokens = GridTokens(["x_0"], dimensionality=self.dimensionality, max_power=2)
+        return [trig_tokens, grid_tokens]
+
+    @staticmethod
+    def noise_data(data, noise_level):
+        return noise_level * 0.01 * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "lv" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    def _save_report(self, report_dir, operator_name, elapsed, search_obj):
+        report = {
+            "scenario": "LotkaVolterra",
+            "operator": operator_name,
+            "noise_level": self.noise_level,
+            "elapsed_sec": elapsed,
+        }
+        (report_dir / "summary.json").write_text(
+            json.dumps(report, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+        raw_clusters = search_obj.equations(only_print=False, num=1)
+        def eq_to_text(eq):
+            return getattr(eq, "text_form", str(eq))
+
+        clusters_json = []
+        for idx, cluster in enumerate(raw_clusters):
+            equations_list = []
+            for eq in cluster:
+                equations_list.append({"equation": eq_to_text(eq)})
+            clusters_json.append({
+                "cluster_id": idx,
+                "size": len(equations_list),
+                "equations": equations_list,
+            })
+
+        (report_dir / "equations.json").write_text(
+            json.dumps(clusters_json, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def make_search(self):
+        t, data = self.lv_data()
+        # обрежем данные, если нужно
+        end = 150   # как в lv_discovery
+        t = t[:end]
+        u = data[:end, 0]
+        v = data[:end, 1]
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            multiobjective_mode=True,
+            use_pic=True,
+            boundary=15,
+            coordinate_tensors=[t],
+            verbose_params={'show_iter_idx': True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=[u, v],
+            variable_names=['u', 'v'],
+            max_deriv_order=1,
+            additional_tokens=self.make_additional_tokens(),
+        )
+        return epde_search_obj
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        t, data = self.lv_data()
+        noised_data = self.noise_data(data, self.noise_level)
+        end = 150
+        t = t[:end]
+        u = noised_data[:end, 0]
+        v = noised_data[:end, 1]
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            multiobjective_mode=True,
+            use_pic=True,
+            boundary=15,
+            coordinate_tensors=[t],
+            verbose_params={'show_iter_idx': True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
+
+        popsize = 16
+        epde_search_obj.set_moeadd_params(population_size=popsize, training_epochs=5)
+
+        factors_max_number = {'factors_num': [1, 2], 'probas': [0.8, 0.2]}
+
+        with Timer() as tim:
+            epde_search_obj.fit(
+                data=[u, v],
+                variable_names=['u', 'v'],
+                max_deriv_order=1,
+                equation_terms_max_number=7,
+                data_fun_pow=3,
+                additional_tokens=self.make_additional_tokens(),
+                equation_factors_max_number=factors_max_number,
+                eq_sparsity_interval=(1e-8, 1e-0),
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, tim.elapsed, epde_search_obj)
+
+        return search_obj, tim.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/lv/t_20.npy b/tests/functional/scenarios/lv/t_20.npy
new file mode 100644
index 00000000..b269f59f
Binary files /dev/null and b/tests/functional/scenarios/lv/t_20.npy differ
diff --git a/tests/functional/scenarios/ns/__init__.py b/tests/functional/scenarios/ns/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/ns/cylinder_nektar_wake.mat b/tests/functional/scenarios/ns/cylinder_nektar_wake.mat
new file mode 100644
index 00000000..0098e409
Binary files /dev/null and b/tests/functional/scenarios/ns/cylinder_nektar_wake.mat differ
diff --git a/tests/functional/scenarios/ns/ns.py b/tests/functional/scenarios/ns/ns.py
new file mode 100644
index 00000000..7a86425b
--- /dev/null
+++ b/tests/functional/scenarios/ns/ns.py
@@ -0,0 +1,165 @@
+import os
+import json
+import torch
+from datetime import datetime
+import numpy as np
+import scipy.io as scio
+import pytest
+from epde.interface.interface import EpdeSearch
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SystemComparison
+from tests.functional.utils.timer import Timer
+
+class NavierStokesTest(EquationTestTemplate):
+    strategy = SystemComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+    def all_vars(self):
+        return ["u", "v", "p"]
+
+    def correct_symbolic(self):
+        eq_u_correct = '0.0001 * d^2u/dx1^2{power: 1.0} + -5.0 * u{power: 3.0} + 5.0 * u{power: 1.0} + 0.0 = du/dx0{power: 1.0}'
+        eq_v_correct = eq_u_correct.replace('u', 'v').replace('du/dx0', 'dv/dx0').replace('d^2u/dx1^2', 'd^2v/dx1^2')
+        eq_p_correct = eq_u_correct.replace('u', 'p').replace('du/dx0', 'dp/dx0').replace('d^2u/dx1^2', 'd^2p/dx1^2')
+        return [eq_u_correct, eq_v_correct, eq_p_correct]
+
+    def incorrect_symbolic(self):
+        eq_u_incorrect = '4.976781518840499 * u{power: 1.0} + 0.0001 * d^2u/dx1^2{power: 1.0} + -4.974425220166616 * u{power: 3.0} + 0.0 * du/dx1{power: 1.0} * d^2u/dx0^2{power: 1.0} + 0.002262543822130977 = du/dx0{power: 1.0}'
+        eq_v_incorrect = eq_u_incorrect.replace('u', 'v').replace('du/dx0', 'dv/dx0').replace('d^2u/dx1^2', 'd^2v/dx1^2').replace('du/dx1', 'dv/dx1')
+        eq_p_incorrect = eq_u_incorrect.replace('u', 'p').replace('du/dx0', 'dp/dx0').replace('d^2u/dx1^2', 'd^2p/dx1^2').replace('du/dx1', 'dp/dx1')
+        return [eq_u_incorrect, eq_v_incorrect, eq_p_incorrect]
+
+    def ns_data(self):
+        """Загружает данные из .mat файла и формирует сетки."""
+        mat = scio.loadmat(os.path.join(self.foldername, 'cylinder_nektar_wake.mat'))
+        U_star = mat['U_star']
+        P_star = mat['p_star']
+        t_star = mat['t']
+        X_star = mat['X_star']
+
+        N = X_star.shape[0]
+        T = t_star.shape[0]
+        t_train = 50                    # как в ns_test
+
+        x = np.unique(X_star[:, 0:1].flatten())
+        y = np.unique(X_star[:, 1:2].flatten())
+        t = t_star.flatten()
+
+        u = U_star[:, 0, :].T.reshape(*t.shape, *y.shape, *x.shape)[:t_train]
+        v = U_star[:, 1, :].T.reshape(*t.shape, *y.shape, *x.shape)[:t_train]
+        p = P_star.T.reshape(*t.shape, *y.shape, *x.shape)[:t_train]
+
+        grids = np.meshgrid(t[:t_train], y, x, indexing='ij')
+        data = [u, v, p]
+        return grids, data
+
+    def noise_data(self, data, noise_level):
+        return [d + noise_level * 0.01 * np.std(d) * np.random.normal(size=d.shape) for d in data]
+
+    def make_additional_tokens(self):
+        return []
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "ns" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    def _save_report(self, report_dir, operator_name, elapsed, search_obj):
+        report = {
+            "scenario": "NavierStokes",
+            "operator": operator_name,
+            "noise_level": self.noise_level,
+            "elapsed_sec": elapsed,
+        }
+        (report_dir / "summary.json").write_text(
+            json.dumps(report, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+        raw_clusters = search_obj.equations(only_print=False, num=1)
+        def eq_to_text(eq):
+            return getattr(eq, "text_form", str(eq))
+
+        clusters_json = []
+        for idx, cluster in enumerate(raw_clusters):
+            equations_list = []
+            for eq in cluster:
+                equations_list.append({"equation": eq_to_text(eq)})
+            clusters_json.append({
+                "cluster_id": idx,
+                "size": len(equations_list),
+                "equations": equations_list,
+            })
+        (report_dir / "equations.json").write_text(
+            json.dumps(clusters_json, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def make_search(self):
+        """Создаёт EpdeSearch для тестового режима (сравнение)."""
+        grids, data = self.ns_data()
+        t, y, x = grids
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            multiobjective_mode=True,
+            use_pic=True,
+            boundary=10,
+            coordinate_tensors=(t, y, x),
+            verbose_params={'show_iter_idx': True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=data,
+            variable_names=['u', 'v', 'p'],
+            max_deriv_order=(2,2,2),
+            additional_tokens=[],
+        )
+        return epde_search_obj
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        """Режим поиска уравнений (discovery)."""
+        grids, data = self.ns_data()
+        noised_data = self.noise_data(data, self.noise_level)
+        t, y, x = grids
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            multiobjective_mode=True,
+            use_pic=True,
+            boundary=[21,21,46],
+            coordinate_tensors=(t, y, x),
+            verbose_params={'show_iter_idx': True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
+
+        popsize = 64
+        epde_search_obj.set_moeadd_params(population_size=popsize, training_epochs=15)
+
+        factors_max_number = {'factors_num': [1, 2], 'probas': [0.8, 0.2]}
+
+        with Timer() as tim:
+            epde_search_obj.fit(
+                data=noised_data,
+                variable_names=['u', 'v', 'p'],
+                max_deriv_order=(1,2,2),
+                equation_terms_max_number=20,
+                data_fun_pow=1,
+                additional_tokens=[],
+                equation_factors_max_number=factors_max_number,
+                eq_sparsity_interval=(1e-12, 1e-0),
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, tim.elapsed, epde_search_obj)
+
+        return search_obj, tim.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/vdp/__init__.py b/tests/functional/scenarios/vdp/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/vdp/vdp.py b/tests/functional/scenarios/vdp/vdp.py
new file mode 100644
index 00000000..fadf597f
--- /dev/null
+++ b/tests/functional/scenarios/vdp/vdp.py
@@ -0,0 +1,133 @@
+import os
+import torch
+import numpy as np
+from datetime import datetime
+import json
+import pytest
+from epde.interface.interface import EpdeSearch
+from epde import TrigonometricTokens, GridTokens
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SingleEquationComparison
+from tests.functional.utils.timer import Timer
+
+class VanDerPolTest(EquationTestTemplate):
+    strategy = SingleEquationComparison()
+
+    def __init__(self, foldername = "", noise_level = 0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+    def all_vars(self):
+        return ["u"]
+
+    def correct_symbolic(self):
+        return "-0.2 * u{power: 2.0} * du/dx0{power: 1.0} + 0.2 * du/dx0{power: 1.0} + -1.0 * u{power: 1.0} + -0.0 = d^2u/dx0^2{power: 1.0}"
+
+    def incorrect_symbolic(self):
+        return '-1.0 * d^2u/dx0^2{power: 1.0} + 1.5 * x_0{power: 1.0, dim: 0.0} + -4.0 * u{power: 1.0} + -0.0 = du/dx0{power: 1.0} * sin{power: 1.0, freq: 2.0, dim: 0.0}'
+
+    def load_data(self):
+        return np.load(os.path.join(self.foldername, "vdp_data.npy"))
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "vdp" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    @staticmethod
+    def make_additional_tokens():
+        trig_tokens = TrigonometricTokens(freq=(2 - 1e-8, 2 + 1e-8), dimensionality=0)
+        grid_tokens = GridTokens(["x_0"], dimensionality=0, max_power=2)
+        return [grid_tokens, trig_tokens]
+
+    @staticmethod
+    def noise_data(data, noise_level):
+        return noise_level * 0.01 * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def make_search(self):
+        step = 0.05
+        steps_num = 320
+        t = np.arange(0., step * steps_num, step)
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            use_pic=True,
+            boundary=2,
+            coordinate_tensors=[t],
+            verbose_params={"show_iter_idx": True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type="FD", preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=self.load_data(),
+            variable_names=["u"],
+            max_deriv_order=(2,),
+            additional_tokens=self.make_additional_tokens(),
+        )
+        return epde_search_obj
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        data = self.load_data()
+        noised_data = self.noise_data(data, self.noise_level)
+
+        search_obj.set_moeadd_params(population_size=16, training_epochs=1)
+
+        with Timer() as t:
+            search_obj.fit(
+                data=[noised_data],
+                variable_names=["u"],
+                max_deriv_order=(2, 3),
+                equation_terms_max_number=5,
+                data_fun_pow=3,
+                additional_tokens=self.make_additional_tokens(),
+                equation_factors_max_number={"factors_num": [1, 2], "probas": [0.65, 0.35]},
+                eq_sparsity_interval=(1e-5, 1e-0),
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+
+            report = {
+                "scenario": "VanDerPol",
+                "operator": operator_name,
+                "noise_level": self.noise_level,
+                "elapsed_sec": t.elapsed,
+            }
+            (report_dir / "summary.json").write_text(
+                json.dumps(report, ensure_ascii=False, indent=2),
+                encoding="utf-8",
+            )
+
+            raw_clusters = search_obj.equations(only_print=False, num=5)
+
+            clusters_json = []
+            for idx, cluster in enumerate(raw_clusters):
+                equations_list = []
+                for eq in cluster:
+                    text = eq.text_form
+                    if "\n" in text:
+                        eq_part, meta_part = text.split("\n", 1)
+                    else:
+                        eq_part, meta_part = text, None
+
+                    equations_list.append({
+                        "equation": eq_part.strip(),
+                        "parameters": meta_part
+                    })
+                clusters_json.append({
+                    "cluster_id": idx,
+                    "size": len(equations_list),
+                    "equations": equations_list
+                })
+
+            # Сохраняем как JSON
+            (report_dir / "equations.json").write_text(
+                json.dumps(clusters_json, ensure_ascii=False, indent=2),
+                encoding="utf-8",
+            )
+
+        return search_obj, t.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/vdp/vdp_ann_pretrained.pickle b/tests/functional/scenarios/vdp/vdp_ann_pretrained.pickle
new file mode 100644
index 00000000..c17f73b8
Binary files /dev/null and b/tests/functional/scenarios/vdp/vdp_ann_pretrained.pickle differ
diff --git a/tests/functional/scenarios/vdp/vdp_data.npy b/tests/functional/scenarios/vdp/vdp_data.npy
new file mode 100644
index 00000000..05a824cf
Binary files /dev/null and b/tests/functional/scenarios/vdp/vdp_data.npy differ
diff --git a/tests/functional/scenarios/wave/__init__.py b/tests/functional/scenarios/wave/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/scenarios/wave/ann_pretrained.pickle b/tests/functional/scenarios/wave/ann_pretrained.pickle
new file mode 100644
index 00000000..7e56ad9d
Binary files /dev/null and b/tests/functional/scenarios/wave/ann_pretrained.pickle differ
diff --git a/tests/functional/scenarios/wave/wave.py b/tests/functional/scenarios/wave/wave.py
new file mode 100644
index 00000000..b13419ff
--- /dev/null
+++ b/tests/functional/scenarios/wave/wave.py
@@ -0,0 +1,149 @@
+import os
+import json
+import torch
+from datetime import datetime
+import numpy as np
+import pytest
+from epde.interface.interface import EpdeSearch
+from tests.functional.templates import EquationTestTemplate
+from tests.functional.comparasion import SingleEquationComparison
+from tests.functional.utils.timer import Timer
+
+class WaveTest(EquationTestTemplate):
+    strategy = SingleEquationComparison()
+
+    def __init__(self, foldername="", noise_level=0):
+        if foldername == "":
+            foldername = os.path.join(os.path.dirname(os.path.realpath(__file__)))
+        self.foldername = foldername
+        self.noise_level = noise_level
+
+    def all_vars(self):
+        return ["u"]
+
+    def correct_symbolic(self):
+        # Волновое уравнение: d^2u/dt^2 = c^2 * d^2u/dx^2, c^2 = 0.04
+        return '0.04 * d^2u/dx1^2{power: 1.0} + 0.0 = d^2u/dx0^2{power: 1.0}'
+
+    def incorrect_symbolic(self):
+        # Неправильное уравнение (близкое, с лишним множителем du/dx0)
+        return '0.04 * d^2u/dx1^2{power: 1.0} * du/dx0{power: 1.0} + 0.0 = d^2u/dx0^2{power: 1.0} * du/dx0{power: 1.0}'
+
+    def wave_data(self):
+        """Загружает данные из CSV и формирует сетки."""
+        shape = 80
+        data = np.loadtxt(os.path.join(self.foldername, 'wave_sln_80.csv'), delimiter=',').T
+        t = np.linspace(0, 1, shape + 1)
+        x = np.linspace(0, 1, shape + 1)
+        grids = np.meshgrid(t, x, indexing='ij')
+        return grids, data
+
+    def noise_data(self, data, noise_level):
+        return noise_level * 0.01 * np.std(data) * np.random.normal(size=data.shape) + data
+
+    def make_additional_tokens(self):
+        return []
+
+    def make_report_dir(self, base_dir, operator_name):
+        stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_dir = base_dir / "wave" / operator_name / stamp
+        report_dir.mkdir(parents=True, exist_ok=True)
+        return report_dir
+
+    def _save_report(self, report_dir, operator_name, elapsed, search_obj):
+        report = {
+            "scenario": "Wave",
+            "operator": operator_name,
+            "noise_level": self.noise_level,
+            "elapsed_sec": elapsed,
+        }
+        (report_dir / "summary.json").write_text(
+            json.dumps(report, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+        raw_clusters = search_obj.equations(only_print=False, num=1)
+        def eq_to_text(eq):
+            return getattr(eq, "text_form", str(eq))
+
+        clusters_json = []
+        for idx, cluster in enumerate(raw_clusters):
+            equations_list = []
+            for eq in cluster:
+                equations_list.append({"equation": eq_to_text(eq)})
+            clusters_json.append({
+                "cluster_id": idx,
+                "size": len(equations_list),
+                "equations": equations_list,
+            })
+
+        (report_dir / "equations.json").write_text(
+            json.dumps(clusters_json, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def make_search(self):
+        """Создаёт объект EpdeSearch для тестового режима (сравнение правильного/неправильного)."""
+        grids, data = self.wave_data()
+        t = grids[0]
+        x = grids[1]
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            multiobjective_mode=True,          # важно для совместимости с SingleEquationComparison
+            use_pic=True,
+            boundary=5,
+            coordinate_tensors=(t, x),
+            verbose_params={'show_iter_idx': True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
+        epde_search_obj.create_pool(
+            data=data,
+            variable_names=['u'],
+            max_deriv_order=(2,2),
+            additional_tokens=[],
+        )
+        return epde_search_obj
+
+    @pytest.mark.slow
+    def run_discovery(self, search_obj, report_dir=None, operator_name="unknown"):
+        """Режим поиска уравнений."""
+        grids, data = self.wave_data()
+        noised_data = self.noise_data(data, self.noise_level)
+        t = grids[0]
+        x = grids[1]
+
+        epde_search_obj = EpdeSearch(
+            use_solver=False,
+            multiobjective_mode=True,
+            use_pic=True,
+            boundary=20,
+            coordinate_tensors=(t, x),
+            verbose_params={'show_iter_idx': True},
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+        )
+        epde_search_obj.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
+
+        popsize = 16
+        epde_search_obj.set_moeadd_params(population_size=popsize, training_epochs=5)
+
+        factors_max_number = {'factors_num': [1, 2], 'probas': [0.65, 0.35]}
+
+        with Timer() as tim:
+            epde_search_obj.fit(
+                data=noised_data,
+                variable_names=['u'],
+                max_deriv_order=(2,3),
+                equation_terms_max_number=5,
+                data_fun_pow=3,
+                additional_tokens=[],
+                equation_factors_max_number=factors_max_number,
+                eq_sparsity_interval=(1e-6, 1e-4),
+            )
+
+        if report_dir is not None:
+            report_dir = self.make_report_dir(report_dir, operator_name)
+            self._save_report(report_dir, operator_name, tim.elapsed, epde_search_obj)
+
+        return search_obj, tim.elapsed
\ No newline at end of file
diff --git a/tests/functional/scenarios/wave/wave_sln_80.csv b/tests/functional/scenarios/wave/wave_sln_80.csv
new file mode 100644
index 00000000..8271838d
--- /dev/null
+++ b/tests/functional/scenarios/wave/wave_sln_80.csv
@@ -0,0 +1,81 @@
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diff --git a/tests/functional/templates.py b/tests/functional/templates.py
new file mode 100644
index 00000000..5709b166
--- /dev/null
+++ b/tests/functional/templates.py
@@ -0,0 +1,39 @@
+from abc import ABC, abstractmethod
+from tests.functional.utils import Timer
+
+class EquationTestTemplate(ABC):
+    strategy = None
+
+    @abstractmethod
+    def make_search(self):
+        pass
+
+    @abstractmethod
+    def correct_symbolic(self):
+        pass
+
+    @abstractmethod
+    def incorrect_symbolic(self):
+        pass
+
+    @abstractmethod
+    def all_vars(self):
+        pass
+
+    def run(self, fit_operator, do_discovery=False):
+        search_obj = self.make_search()
+
+        if do_discovery:
+            return self.run_discovery(search_obj)
+
+        correct_obj = self.strategy.build(self.correct_symbolic(), search_obj, self.all_vars())
+        incorrect_obj = self.strategy.build(self.incorrect_symbolic(), search_obj, self.all_vars())
+
+        with Timer() as t:
+            ok = self.strategy.compare(correct_obj, incorrect_obj, fit_operator, self.all_vars())
+
+        return ok, t.elapsed
+
+
+    def run_discovery(self, search_obj):
+        raise NotImplementedError
\ No newline at end of file
diff --git a/tests/functional/tests/test_ac.py b/tests/functional/tests/test_ac.py
new file mode 100644
index 00000000..b793e521
--- /dev/null
+++ b/tests/functional/tests/test_ac.py
@@ -0,0 +1,47 @@
+import pytest
+
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.ac.ac import ACTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_ac(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = ACTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_burgers_discovery.py b/tests/functional/tests/test_burgers_discovery.py
new file mode 100644
index 00000000..88e530c0
--- /dev/null
+++ b/tests/functional/tests/test_burgers_discovery.py
@@ -0,0 +1,47 @@
+import pytest
+
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.burgers.burgers import BurgersTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_burgers_discovery(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = BurgersTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_burgers_sindy.py b/tests/functional/tests/test_burgers_sindy.py
new file mode 100644
index 00000000..3f204dd2
--- /dev/null
+++ b/tests/functional/tests/test_burgers_sindy.py
@@ -0,0 +1,47 @@
+import pytest
+
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.burgers.burgers import BurgersTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_burgers_sindy(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = BurgersTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_sindy_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_kdv_discovery.py b/tests/functional/tests/test_kdv_discovery.py
new file mode 100644
index 00000000..12924f56
--- /dev/null
+++ b/tests/functional/tests/test_kdv_discovery.py
@@ -0,0 +1,48 @@
+import pytest
+
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.kdv.kdv import KdVTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_kdv(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = KdVTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_kdv_sindy.py b/tests/functional/tests/test_kdv_sindy.py
new file mode 100644
index 00000000..82dcbd21
--- /dev/null
+++ b/tests/functional/tests/test_kdv_sindy.py
@@ -0,0 +1,48 @@
+import pytest
+
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.kdv.kdv import KdVTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_kdv_discovery_sindy(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = KdVTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()  # если для sindy нужен отдельный pool, см. ниже
+        search_obj, elapsed = scenario.run_sindy_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_ks.py b/tests/functional/tests/test_ks.py
new file mode 100644
index 00000000..cddad23e
--- /dev/null
+++ b/tests/functional/tests/test_ks.py
@@ -0,0 +1,48 @@
+import pytest
+
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.ks.ks import KSTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_ks(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = KSTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_lorenz.py b/tests/functional/tests/test_lorenz.py
new file mode 100644
index 00000000..62e9b101
--- /dev/null
+++ b/tests/functional/tests/test_lorenz.py
@@ -0,0 +1,48 @@
+import pytest
+
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.lorenz.lorenz import LorenzTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_lorenz(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = LorenzTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_lv.py b/tests/functional/tests/test_lv.py
new file mode 100644
index 00000000..61362d85
--- /dev/null
+++ b/tests/functional/tests/test_lv.py
@@ -0,0 +1,46 @@
+import pytest
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.lv.lv import LotkaVolterraTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_lv(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = LotkaVolterraTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_ns.py b/tests/functional/tests/test_ns.py
new file mode 100644
index 00000000..7c1bd31f
--- /dev/null
+++ b/tests/functional/tests/test_ns.py
@@ -0,0 +1,45 @@
+import pytest
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.ns.ns import NavierStokesTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_ns(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = NavierStokesTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_vdp.py b/tests/functional/tests/test_vdp.py
new file mode 100644
index 00000000..f7537c7f
--- /dev/null
+++ b/tests/functional/tests/test_vdp.py
@@ -0,0 +1,46 @@
+import pytest
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.vdp.vdp import VanDerPolTest
+
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_vdp(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = VanDerPolTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/tests/test_wave.py b/tests/functional/tests/test_wave.py
new file mode 100644
index 00000000..505a7a85
--- /dev/null
+++ b/tests/functional/tests/test_wave.py
@@ -0,0 +1,46 @@
+import pytest
+from tests.functional.operator_factory import FitnessOperatorFactory
+from tests.functional.scenarios.wave.wave import WaveTest
+from epde.operators.utils.default_parameter_loader import EvolutionaryParams
+
+operator_params_l2lr = EvolutionaryParams().get_default_params_for_operator(
+    "DiscrepancyBasedFitnessWithCV"
+)
+
+operator_params_deepxde = EvolutionaryParams().get_default_params_for_operator(
+    'DeepXDEBasedFitness'
+)
+
+operator_params_pic = EvolutionaryParams().get_default_params_for_operator(
+    'PIC'
+)
+
+ALL_CASES = [
+    ("DeepXDEBasedFitness", operator_params_deepxde),
+    ("PIC", operator_params_pic),
+    ("L2LRFitness", operator_params_l2lr),
+]
+
+@pytest.mark.functional
+@pytest.mark.parametrize("operator_name, params", ALL_CASES)
+def test_wave(operator_name, params, runtime_options):
+    import epde.globals as global_var
+    global_var.solution_guess_nn = None
+    if operator_name not in runtime_options["operators"]:
+        pytest.skip(f"{operator_name} skipped by --operators")
+
+    operator = FitnessOperatorFactory.create(operator_name, params)
+    scenario = WaveTest(noise_level=0)
+
+    if runtime_options["discovery"]:
+        search_obj = scenario.make_search()
+        search_obj, elapsed = scenario.run_discovery(
+            search_obj,
+            report_dir=runtime_options["report_dir"] if runtime_options["report"] else None,
+            operator_name=operator_name,
+        )
+        assert elapsed < 600
+    else:
+        ok, elapsed = scenario.run(operator)
+        assert ok
+        assert elapsed < 60
\ No newline at end of file
diff --git a/tests/functional/utils/__init__.py b/tests/functional/utils/__init__.py
new file mode 100644
index 00000000..c3347407
--- /dev/null
+++ b/tests/functional/utils/__init__.py
@@ -0,0 +1 @@
+from .timer import Timer
\ No newline at end of file
diff --git a/tests/functional/utils/timer.py b/tests/functional/utils/timer.py
new file mode 100644
index 00000000..db15d315
--- /dev/null
+++ b/tests/functional/utils/timer.py
@@ -0,0 +1,9 @@
+from time import perf_counter
+
+class Timer:
+    def __enter__(self):
+        self.start = perf_counter()
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        self.elapsed = perf_counter() - self.start
\ No newline at end of file