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Merged
Yaroslav-Muravev merged 1 commit into from
Jul 28, 2026
Merged

Current version of Unified DeepXDE #7

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24 changes: 12 additions & 12 deletions epde/integrate/deepxde_integration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,16 +117,16 @@ def func(x):

if len(left_idx) > 0:
bcs.append(dde.icbc.DirichletBC(geomtime, make_bc_func(left_idx),
lambda _, on_boundary: on_boundary and np.isclose(_.x[0], x.min(),
lambda _, on_boundary: on_boundary and np.isclose(_[0], x.min(),
rtol=1e-5,
atol=eps_x),
component=var_idx))
component=var_idx)) # Заменил _.x[0] на _[0]
if len(right_idx) > 0:
bcs.append(dde.icbc.DirichletBC(geomtime, make_bc_func(right_idx),
lambda _, on_boundary: on_boundary and np.isclose(_.x[0], x.max(),
lambda _, on_boundary: on_boundary and np.isclose(_[0], x.max(),
rtol=1e-5,
atol=eps_x),
component=var_idx))
component=var_idx)) # Заменил _.x[0] на _[0]
if len(initial_idx) > 0:
bcs.append(dde.icbc.IC(geomtime, make_bc_func(initial_idx),
lambda _, on_initial: on_initial,
Expand Down Expand Up @@ -197,28 +197,28 @@ def func(x):

if len(x_min_idx) > 0:
bcs.append(dde.icbc.DirichletBC(geomtime, make_bc_func(x_min_idx),
lambda _, on_boundary: on_boundary and np.isclose(_.x[0], x.min(),
lambda _, on_boundary: on_boundary and np.isclose(_[0], x.min(),
rtol=1e-5,
atol=eps_x),
component=var_idx))
component=var_idx)) # Заменил _.x[0] на _[0]
if len(x_max_idx) > 0:
bcs.append(dde.icbc.DirichletBC(geomtime, make_bc_func(x_max_idx),
lambda _, on_boundary: on_boundary and np.isclose(_.x[0], x.max(),
lambda _, on_boundary: on_boundary and np.isclose(_[0], x.max(),
rtol=1e-5,
atol=eps_x),
component=var_idx))
component=var_idx)) # Заменил _.x[0] на _[0]
if len(y_min_idx) > 0:
bcs.append(dde.icbc.DirichletBC(geomtime, make_bc_func(y_min_idx),
lambda _, on_boundary: on_boundary and np.isclose(_.x[1], y.min(),
lambda _, on_boundary: on_boundary and np.isclose(_[1], y.min(),
rtol=1e-5,
atol=eps_y),
component=var_idx))
component=var_idx)) # Заменил _.x[1] на _[1]
if len(y_max_idx) > 0:
bcs.append(dde.icbc.DirichletBC(geomtime, make_bc_func(y_max_idx),
lambda _, on_boundary: on_boundary and np.isclose(_.x[1], y.max(),
lambda _, on_boundary: on_boundary and np.isclose(_[1], y.max(),
rtol=1e-5,
atol=eps_y),
component=var_idx))
component=var_idx)) # Заменил _.x[1] на _[1]
if len(initial_idx) > 0:
bcs.append(dde.icbc.IC(geomtime, make_bc_func(initial_idx),
lambda _, on_initial: on_initial,
Expand Down
84 changes: 84 additions & 0 deletions epde/solver/poka_tut_pobudem/wave.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,84 @@
import numpy as np
import copy
from epde.interface.interface import EpdeSearch
from epde.interface.equation_translator import translate_equation
from epde.structure.main_structures import SoEq, Chromosome
from epde.interface.token_family import TFPool
from epde.operators.utils.default_parameter_loader import EvolutionaryParams
from epde.solver.factory import SolverFactory

def create_equation_from_str(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 solve_with_solver(solver_type, solver_config, system, grids, data):
adapter = SolverFactory.create(solver_type, **solver_config)
solutions, loss = adapter.solve(system, grids, data)
return solutions, loss

# ----------------------------------------------------------------------
# Волновое уравнение (PDE) – только DeepXDE
# ----------------------------------------------------------------------
print("=" * 60)
print("Wave equation (PDE) – DeepXDE PINN solver")
print("=" * 60)

# Генерация данных (аналитическое решение)
nx, nt = 50, 50
x = np.linspace(0, 1, nx)
t = np.linspace(0, 2, nt)
X_grid, T_grid = np.meshgrid(t, x, indexing='ij')
exact = np.sin(np.pi * X_grid) * np.cos(np.pi * T_grid)
data_wave = exact + 0.01 * np.random.normal(size=exact.shape)

# Создание пула EPDE
search_wave = EpdeSearch(
use_solver=False,
coordinate_tensors=(T_grid, X_grid),
verbose_params={'show_iter_idx': False},
device='cpu'
)
search_wave.set_preprocessor(default_preprocessor_type='FD', preprocessor_kwargs={})
search_wave.create_pool(
data=data_wave,
variable_names=['u'],
max_deriv_order=(2, 2),
additional_tokens=[]
)

# Уравнение волновое
eq_str = '1.0 * d^2u/dx1^2{power: 1.0} = d^2u/dx0^2{power: 1.0}'
soeq_wave = translate_equation(eq_str, search_wave.pool, all_vars=['u'])
eq_wave = soeq_wave.vals['u']
eq_wave.main_var_to_explain = 'u'
eq_wave.weights_internal = np.ones(len(eq_wave.structure) - 1)
eq_wave.weights_internal_evald = True
eq_wave.weights_final_evald = True

system_wave = SoEq(search_wave.pool, {})
system_wave.vals = Chromosome({'u': eq_wave}, {})
system_wave.moeadd_set = True

# Конфигурация DeepXDE (увеличиваем параметры для PDE)
solver_config_deepxde = EvolutionaryParams().get_default_params_for_operator('DeepXDEBasedFitness')
solver_config_deepxde['num_domain'] = 2000
solver_config_deepxde['num_boundary'] = 500
solver_config_deepxde['num_initial'] = 500
solver_config_deepxde['epochs'] = 3000

solutions_wave, loss_wave = solve_with_solver(
"deepxde",
solver_config_deepxde,
system_wave,
[T_grid, X_grid],
[data_wave.flatten()]
)

soln_wave = solutions_wave[0].reshape(data_wave.shape)
print(f"Loss (RMSE): {loss_wave:.6f}")
print(f"Max error: {np.max(np.abs(soln_wave - exact)):.6f}")
6 changes: 4 additions & 2 deletions tests/functional/operator_factory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,8 +40,10 @@ def create(name: str, params: dict) -> CompoundOperator:
operator = SolverBasedFitness(list(params.keys()), objectives=[primary],
primary=primary, stability=Instability(),
backend='deepxde')
sparsity = LASSOSparsity()
coeff_calc = LinRegBasedCoeffsEquation()
#sparsity = LASSOSparsity()
#coeff_calc = LinRegBasedCoeffsEquation()
sparsity = map_operator_between_levels(LASSOSparsity(), 'gene level', 'chromosome level')
coeff_calc = map_operator_between_levels(LinRegBasedCoeffsEquation(), 'gene level', 'chromosome level')
else:
raise ValueError(f"Unknown operator: {name}")

Expand Down
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