diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
index 3e3a7419..bf359241 100644
--- a/.github/workflows/run_tests.yml
+++ b/.github/workflows/run_tests.yml
@@ -271,8 +271,50 @@ jobs:
           files: coverage*.xml
           fail_ci_if_error: true
 
+  tests-demos:
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        # test with oldest supported Python version only (for slow tests)
+        python-version: ["3.10"]
+
+        demo:
+          - data-assimilation-4dvar
+
+      fail-fast: false
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+      - name: Set up Git repository
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Restore UV environment
+        run: cp production.uv.lock uv.lock
+
+      - name: Install dev requirements
+        run: |
+          uv sync --extra dev --frozen
+
+      - name: Run demos
+        working-directory: demo/${{matrix.demo}}
+        run: |
+          uv pip install jupyter
+          uv run --no-sync jupyter nbconvert --to notebook --execute demo.ipynb
+
+
   all-ok:
-    needs: [tests-base, tests-e2e]
+    needs: [tests-base, tests-e2e, tests-demos]
 
     runs-on: ubuntu-latest
 
@@ -289,4 +331,9 @@ jobs:
             exit 1
           fi
 
+          if [ "${{ needs.tests-demos.result }}" != "success" ]; then
+            echo "Demo tests failed"
+            exit 1
+          fi
+
           echo "All tests passed!"
diff --git a/demo/data-assimilation-4dvar/demo.ipynb b/demo/data-assimilation-4dvar/demo.ipynb
new file mode 100644
index 00000000..8b7b2290
--- /dev/null
+++ b/demo/data-assimilation-4dvar/demo.ipynb
@@ -0,0 +1,855 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4D-Variational Data Assimilation for a Chaotic Dynamical System\n",
+    "\n",
+    "In this tutorial, you will learn how to use differentiable Tesseracts to implement a 4D-Var data assimilation scheme for a chaotic dynamical system, which includes using Tesseracts in an optimization context and embedding them into other (differentiable) Python / JAX code. \n",
+    "\n",
+    "We will use the [Lorenz 96](https://en.wikipedia.org/wiki/Lorenz_96_model) model as our dynamical system and demonstrate how to combine model forecasts with observations to produce an optimal estimate of the state of the system over a time window.\n",
+    "\n",
+    "## Context\n",
+    "Here we walk throught a simple, hands-on demo of [four-dimensional variational data assimilation](https://www.ecmwf.int/en/newsletter/175/earth-system-science/linearised-physics-heart-ecmwfs-4d-var) -- sometimes called 4D-Var -- using a classic chaotic system.\n",
+    "\n",
+    "4D-Var is a powerful technique for combining model forecasts with observations to produce an optimal estimate of the state of a dynamical system over a time window. It is widely used in meteorology, oceanography, and other fields where accurate state estimation is crucial.\n",
+    "\n",
+    "It is also a great fit for Tesseracts:\n",
+    "- **Separation of model and optimization** --- Tesseracts allow us to define the model as a separate component from the optimization process, making it easy to swap out different models or modify the model without changing the optimization code.\n",
+    "- **Autodiff capabilities** --- 4D-Var is a variational method, which means it can be expressed as an optimization problem that minimizes a cost function. Since Tesseracts expose their derivatives, we can easily compute gradients of the cost function with respect to the model state and parameters.\n",
+    "- **JAX interoperability** --- Tesseracts play well with [JAX](https://github.com/jax-ml/jax), which provides powerful tools for automatic differentiation and GPU/TPU acceleration. This allows us to efficiently compute gradients and perform optimization on large-scale problems. \n",
+    "\n",
+    "## Problem setup\n",
+    "\n",
+    "We’ll use data from a two-scale Lorenz 96 system, which describes two sets of variables operating on different scales, but run a simpler, single-scale Lorenz 96 solver as our surrogate model. That is, we will use a model that we know is incorrect to illustrate how 4D-Var can still produce useful results even when the model does not perfectly match the true system.\n",
+    "\n",
+    "**Two‐scale Lorenz 96**  \n",
+    "\n",
+    "The true system couples each slow variable $x_i$ with $J$ fast variables $y_{j,i}$ through the following set of ordinary differential equations:\n",
+    "\n",
+    "$$\n",
+    "\\frac{dx_i}{dt}\n",
+    "= (x_{i+1} - x_{i-2})\\,x_{i-1}\n",
+    "- x_i\n",
+    "+ F\n",
+    "- \\frac{h\\,c}{b}\\sum_{j=1}^{J}y_{j,i},\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\frac{dy_{j,i}}{dt}\n",
+    "= c\\,b\\,(y_{j+1,i} - y_{j-2,i})\\,y_{j-1,i}\n",
+    "- c\\,y_{j,i}\n",
+    "+ \\frac{h\\,c}{b}\\,x_i,\n",
+    "$$\n",
+    "\n",
+    "where $F$ is the external forcing, $h$ controls the coupling strength, $c$ is the slow/fast time‐scale ratio, and $b$ sets the fast‐variable amplitude. For the true system, we use $h=1$, $b=10$, $c=10$, and $F=18$. \n",
+    "\n",
+    "The fast variables in the true system have small amplitude yet are essential for accurate forecasts—just as in many multi‐scale or multi‐physics problems. Because limited computational power and imperfect knowledge prevent fully resolving small‐scale, fast processes in real science and engineering simulations, we instead employ a simplified, reduced‐order model -- in this case the single‐scale Lorenz system—to approximate the true dynamics.\n",
+    "\n",
+    "**Single‐scale Lorenz 96**  \n",
+    "\n",
+    "For $i=1,\\dots,N$ (with cyclic indices $x_{i+N}=x_i$):\n",
+    "\n",
+    "$$\n",
+    "\\frac{dx_i}{dt}\n",
+    "= (x_{i+1} - x_{i-2})\\,x_{i-1}\n",
+    "- x_i\n",
+    "+ F,\n",
+    "$$\n",
+    "\n",
+    "where $N$ is the number of slow variables. We can augment the reduced-order model by adding an empirical correction term as a function of the reduced-order model states to partially compensate for the missing dynamics; however, since the true fast processes obey their own ODEs, this closure remains approximate and cannot fully eliminate model error. In complex nonlinear systems, these small model errors amplify rapidly -- much like weather forecasts lose skill beyond a few days -- so the predicted state inevitably degrades over time.\n",
+    "\n",
+    "Now, let’s have a look at how an imperfect model can deviate from the truth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install additional packages\n",
+    "%pip install -r requirements.txt -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Build and serve the single-scale Lorenz 96 Tesseract\n",
+    "\n",
+    "We implement a simple JAX model in the `lorenz_tesseract` folder (see \n",
+    "[docs](lorenz_tesseract.md)). The `apply` function returns a trajectory of physical states given an initial condition:\n",
+    "\n",
+    "```python\n",
+    "def lorenz96_multi_step(\n",
+    "    state: jnp.ndarray, F: float, dt: float, n_steps: int\n",
+    ") -> jnp.ndarray:\n",
+    "    \"\"\"Perform multiple steps of Lorenz 96 integration using scan.\"\"\"\n",
+    "\n",
+    "    def step_fn(state: jnp.ndarray, _: Any) -> tuple:\n",
+    "        return lorenz96_step(state, F, dt), state\n",
+    "\n",
+    "    _, trajectory = jax.lax.scan(step_fn, state, None, length=n_steps)\n",
+    "    return trajectory\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the `tesseract build` command to build the Tesseract into a Docker image, which we can then run as a service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[?25l \u001b[1;2m[\u001b[0m\u001b[34mi\u001b[0m\u001b[1;2m]\u001b[0m Building image \u001b[33m...\u001b[0m\n",
+      "\u001b[2K\u001b[37m⠋\u001b[0m \u001b[37mProcessing\u001b[0m\n",
+      "\u001b[1A\u001b[2K \u001b[1;2m[\u001b[0m\u001b[34mi\u001b[0m\u001b[1;2m]\u001b[0m Built image sh\u001b[1;92ma256:8237\u001b[0m1ea43228, \u001b[1m[\u001b[0m\u001b[32m'lorenz:latest'\u001b[0m\u001b[1m]\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\"lorenz:latest\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "tesseract build lorenz_tesseract/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start a server container to interact with the Tesseract.\n",
+    "from tesseract_core import Tesseract\n",
+    "\n",
+    "lorenz_tesseract = Tesseract.from_image(\"lorenz\")\n",
+    "lorenz_tesseract.serve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Load the ground truth data\n",
+    "\n",
+    "[This](https://github.com/pasteurlabs/tesseract-core/blob/main/demo/data-assimilation-4dvar/lorenz96_two_scale_F_18_sample_0_small.npz) is the data we will use to perform the 4D-Var assimilation. The ground truth is generated by integrating the two-scale Lorenz 96 system with a known initial condition and parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "X_states = np.load(\"lorenz96_two_scale_F_18_sample_0_small.npz\")[\"X_states\"]\n",
+    "t = np.load(\"lorenz96_two_scale_F_18_sample_0_small.npz\")[\"t\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Forward evaluation with Tesseract, create observations\n",
+    "\n",
+    "Let's use our imperfect surrogate to perform a simulation from the true intial condition. We will set the forcing term $F=18$, and we use $dt=0.005$ for numerical integration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jax.numpy as jnp\n",
+    "\n",
+    "static_params = {\n",
+    "    \"F\": 18.0,\n",
+    "    \"dt\": 0.005,\n",
+    "    \"n_steps\": 2000,\n",
+    "}\n",
+    "\n",
+    "# remove the first 500 time steps to avoid transients\n",
+    "true_trajectory = X_states[500:]\n",
+    "X0 = true_trajectory[0]\n",
+    "\n",
+    "\n",
+    "def lorenz_tesseract_fn(x0: jnp.ndarray) -> jnp.ndarray:\n",
+    "    \"\"\"Forward operator that integrates the Lorenz 96 system using a surrogate model.\n",
+    "\n",
+    "    Args:\n",
+    "        x0: Initial state vector of the Lorenz 96 system\n",
+    "\n",
+    "    Returns:\n",
+    "        Complete trajectory of the system state after time integration\n",
+    "    \"\"\"\n",
+    "    return lorenz_tesseract.apply(\n",
+    "        inputs=dict(state=x0, **static_params),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "pred = lorenz_tesseract_fn(X0)[\"result\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "H = jnp.eye(8)  # observe all variables\n",
+    "\n",
+    "# Create synthetic observations\n",
+    "key = jax.random.PRNGKey(0)\n",
+    "\n",
+    "# time step between observations\n",
+    "obs_gap = 50\n",
+    "time_obs = jnp.arange(obs_gap, len(true_trajectory), obs_gap)\n",
+    "obs = true_trajectory[obs_gap::obs_gap]\n",
+    "obs = jax.vmap(lambda row: H @ row)(obs)\n",
+    "\n",
+    "# Observation error covariance matrix\n",
+    "std_obs = 0.5\n",
+    "R = std_obs**2 * jnp.eye(obs.shape[1])\n",
+    "sqrt_s = jnp.sqrt(R)\n",
+    "noise = jax.random.normal(key, obs.shape) @ sqrt_s\n",
+    "obs = obs + noise\n",
+    "\n",
+    "# visualize the first slow variable\n",
+    "pos = 0\n",
+    "plot_end = 2000\n",
+    "plot_obs_end = plot_end // obs_gap\n",
+    "plt.figure(figsize=(10, 4))\n",
+    "plt.plot(pred[:plot_end, pos], label=\"Surrogate $X_0$\")\n",
+    "plt.plot(true_trajectory[:plot_end, pos], label=\"True $X_{0}$\")\n",
+    "plt.plot(time_obs[:plot_obs_end], obs[:plot_obs_end, pos], \"o\", label=\"Observations\")\n",
+    "\n",
+    "plt.xlabel(\"Time Steps\")\n",
+    "plt.ylabel(\"Amplitude\")\n",
+    "plt.title(\"Lorenz 96 Variable $X_0$\")\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With an imperfect surrogate, the model’s trajectory only remains close to the true system for a short time. But fortunately, we have real observations! Balloon soundings, satellite measurements, and the like—that, despite being noisy and sparse, still provide invaluable information. 4D-Var is a data-assimilation technique that blends these observations with our model forecasts to yield a more accurate state estimate, which then initializes the next forecast cycle.\n",
+    "\n",
+    "This is the same approach [used operationally at the European Centre for Medium-Range Weather Forecasts](https://www.ecmwf.int/en/about/media-centre/news/2017/20-years-4d-var-better-forecasts-through-better-use-observations#:~:text=8%20years%20old.-,20%20years%20of%204D%2DVar%3A%20better%20forecasts%20through,a%20better%20use%20of%20observations&text=Twenty%20years%20ago%20ECMWF%20added,improvements%20in%20the%20Centre's%20forecasts.), one of the world’s premier weather-prediction centres.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: 4D-Var data assimilation\n",
+    "\n",
+    "In this section, we will implement the 4D-Var algorithm to assimilate the observations into our model. The goal is to find an initial condition that minimizes the difference between the model trajectory and the observations over a given time window."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Introduction to the 4D-Var method\n",
+    "\n",
+    "<div class=admonition note alert alert-warning>\n",
+    "<p class=admonition-title>Note</p>\n",
+    "    \n",
+    "You can skip this section if you wish -- all you need to know for the purpose of this tutorial is that 4D-Var can be represented as an optimization problem that minimizes a cost function over a time window, using the model to propagate the state forward in time. But if you want to understand the method in more detail, read on!\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "Given an imperfect forecast $\\mathbf{x}^b$ (background), 4D-Var finds the initial state $\\mathbf{x}_0$ that minimizes\n",
+    "\n",
+    "$$\n",
+    "J(\\mathbf{x}_0)\n",
+    "= \\frac{1}{2}(\\mathbf{x}_0 - \\mathbf{x}^b)^\\top \\mathbf{B}^{-1} (\\mathbf{x}_0 - \\mathbf{x}^b)\n",
+    "\\;+\\;\n",
+    "\\frac{1}{2}\n",
+    "\\sum_{i=0}^{n_{\\mathrm{obs}}-1}\n",
+    "\\bigl(\\mathbf{y}_i - \\mathbf{H}\\,\\mathbf{x}_i\\bigr)^\\top\n",
+    "\\mathbf{R}^{-1}\n",
+    "\\bigl(\\mathbf{y}_i - \\mathbf{H}\\,\\mathbf{x}_i\\bigr),\n",
+    "$$\n",
+    "\n",
+    "subject to\n",
+    "\n",
+    "$$\n",
+    "\\mathbf{x}_i =\n",
+    "\\begin{cases}\n",
+    "\\mathbf{x}_0, & i = 0,\\\\\n",
+    "\\mathcal{M}(\\mathbf{x}_{i-1}), & i = 1,\\dots,n_{\\mathrm{obs}}-1.\n",
+    "\\end{cases}\n",
+    "$$\n",
+    "\n",
+    "**Definition of symbols**  \n",
+    "- $\\mathbf{x}_0\\in\\mathbb{R}^n$: the **analysis** (initial) state vector at the start of the window.  \n",
+    "- $\\mathbf{x}^b\\in\\mathbb{R}^n$: the **background** or first-guess state (short-range forecast).  \n",
+    "- $\\mathbf{B}\\in\\mathbb{R}^{n\\times n}$: background-error covariance matrix.  \n",
+    "- $n_{\\mathrm{obs}}$: number of observation times in the assimilation window.  \n",
+    "- $\\mathbf{y}_i\\in\\mathbb{R}^m$: observation vector at time $t_i$.  \n",
+    "- $\\mathbf{H}\\in\\mathbb{R}^{m\\times n}$: (linearized) observation operator mapping state $\\mathbf{x}_i$ to observation space.  \n",
+    "- $\\mathbf{R}\\in\\mathbb{R}^{m\\times m}$: observation-error covariance matrix.  \n",
+    "- $\\mathcal{M}:\\mathbb{R}^n\\to\\mathbb{R}^n$: nonlinear forecast model operator advancing the state from $t_{i-1}$ to $t_i$.  \n",
+    "- $\\mathbf{x}_i$: model state at time $t_i$, with $\\mathbf{x}_0$ as the control variable and $\\mathbf{x}_i = \\mathcal{M}(\\mathbf{x}_{i-1})$ thereafter.  \n",
+    "- $i$: index for observation times, ranging from 0 to $n_{\\mathrm{obs}}-1$.\n",
+    "\n",
+    "\n",
+    "#### What is being minimized?\n",
+    "\n",
+    "\n",
+    "1. **Observation‐misfit**\n",
+    "\n",
+    "   $$\n",
+    "   \\frac{1}{2}\n",
+    "   \\sum_{i=0}^{n_{\\mathrm{obs}}-1}\n",
+    "   \\bigl(\\mathbf{y}_i - \\mathbf{H}\\,\\mathbf{x}_i\\bigr)^\\top\n",
+    "   \\mathbf{R}^{-1}\n",
+    "   \\bigl(\\mathbf{y}_i - \\mathbf{H}\\,\\mathbf{x}_i\\bigr)\n",
+    "   $$\n",
+    "\n",
+    "   Enforces agreement with all observations, weighted by the observation‐error covariance $\\mathbf{R}$. However, real observations are always sparse and noisy, so minimizing this term alone is ill‐conditioned and admits infinitely many solutions.\n",
+    "\n",
+    "2. **Background‐misfit**  \n",
+    "   \n",
+    "   $$\n",
+    "   \\frac{1}{2}(\\mathbf{x}_0 - \\mathbf{x}^b)^\\top \\mathbf{B}^{-1} (\\mathbf{x}_0 - \\mathbf{x}^b)\n",
+    "   $$\n",
+    "\n",
+    "   Penalizes deviations from the imperfect forecast, scaled by the background‐error covariance $\\mathbf{B}$. This term also acts as a regularizer, making the overall optimization well‐posed.\n",
+    "\n",
+    "\n",
+    "#### Why not simply re-initialize the model with observations?\n",
+    "\n",
+    "There are many reasons -- here are two of them:\n",
+    "- **Noisy and sparse observations.** Direct insertion of $\\mathbf{y}$ injects noise and leaves unobserved components unconstrained.  \n",
+    "- **Temporal smoothing.** 4D-Var distributes corrections over the entire window, yielding a dynamically consistent analysis that tries to satisfy both model physics and all observations.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4D-Var implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# idealized error convariance matrices\n",
+    "B_inv = jnp.eye(8)\n",
+    "R_inv = jnp.linalg.inv(R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create 6 groups of 3 observations\n",
+    "n_obs = 3\n",
+    "obs_trajs = [obs[i : i + n_obs] for i in range(0, 19, n_obs)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params_window = {\n",
+    "    \"F\": 18.0,\n",
+    "    \"dt\": 0.005,\n",
+    "    \"n_steps\": obs_gap * n_obs,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def M_window(x: jnp.ndarray) -> jnp.ndarray:\n",
+    "    \"\"\"Forward operator integrating Lorenz 96 over an entire assimilation window.\n",
+    "\n",
+    "    Args:\n",
+    "        x: Initial state vector at the beginning of the assimilation window\n",
+    "\n",
+    "    Returns:\n",
+    "        Complete trajectory spanning the entire assimilation window\n",
+    "    \"\"\"\n",
+    "    return lorenz_tesseract.apply(\n",
+    "        inputs=dict(state=x, **params_window),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We implement the optimization algorithm using SciPy and JAX. That is, we define the cost function $J(\\mathbf{x}_0)$ and use JAX's automatic differentiation to compute the gradient with respect to the initial state $\\mathbf{x}_0$.\n",
+    "\n",
+    "We then use an optimization algorithm that leverages these gradients (e.g., L-BFGS) to minimize the cost function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy\n",
+    "\n",
+    "\n",
+    "def analysis_4DVar(\n",
+    "    xb: jnp.ndarray,\n",
+    "    y: jnp.ndarray,\n",
+    "    M: callable,\n",
+    "    H: jnp.ndarray,\n",
+    "    R_inv: jnp.ndarray,\n",
+    "    B_inv: jnp.ndarray,\n",
+    "    n_obs: int = 3,\n",
+    ") -> jnp.ndarray:\n",
+    "    \"\"\"Perform four-dimensional variational data assimilation analysis.\n",
+    "\n",
+    "    Args:\n",
+    "        xb: Background state vector serving as the first guess\n",
+    "        y: Observation vector containing measurements at multiple time steps\n",
+    "        M: Forward model operator for time integration between observations\n",
+    "        H: Linear observation operator mapping model space to observation space\n",
+    "        R_inv: Inverse observation error covariance matrix\n",
+    "        B_inv: Inverse background error covariance matrix\n",
+    "        learning_rate: Step size for Adam optimizer\n",
+    "        max_iter: Maximum number of optimization iterations\n",
+    "        n_obs: Number of observation times within the assimilation window\n",
+    "\n",
+    "    Returns:\n",
+    "        Optimal analysis state minimizing the 4D-Var cost function\n",
+    "    \"\"\"\n",
+    "\n",
+    "    @jax.jit\n",
+    "    def cost_fn(x0: jnp.ndarray) -> jnp.ndarray:\n",
+    "        \"\"\"Compute the 4D-Var cost function with background and observation terms.\n",
+    "\n",
+    "        Evaluates the standard 4D-Var objective function J = J_b + J_o, where J_b\n",
+    "        penalizes departure from background and J_o penalizes observation misfits\n",
+    "        across the assimilation window.\n",
+    "\n",
+    "        Args:\n",
+    "            x0: Initial state vector for cost evaluation\n",
+    "\n",
+    "        Returns:\n",
+    "            Total cost function value combining background and observation terms\n",
+    "        \"\"\"\n",
+    "        J_b = 0.0\n",
+    "        J_o = 0.0\n",
+    "\n",
+    "        # Background term\n",
+    "        background_diff = x0 - xb\n",
+    "        J_b = 0.5 * jnp.dot(background_diff, B_inv @ background_diff)\n",
+    "\n",
+    "        # Observation terms across assimilation window\n",
+    "        current_state = x0\n",
+    "        obs_diff = y[0] - H @ current_state\n",
+    "        J_o += 0.5 * jnp.dot(obs_diff, R_inv @ obs_diff)\n",
+    "\n",
+    "        for i in range(1, n_obs):\n",
+    "            current_state = M(current_state)[\"result\"][-1]\n",
+    "            obs_diff = y[i] - H @ current_state\n",
+    "            J_o += 0.5 * jnp.dot(obs_diff, R_inv @ obs_diff)\n",
+    "\n",
+    "        return J_b + J_o\n",
+    "\n",
+    "    x0 = scipy.optimize.minimize(\n",
+    "        cost_fn, xb, method=\"L-BFGS-B\", jac=jax.grad(cost_fn)\n",
+    "    ).x\n",
+    "    return x0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But wait! Our model is a Tesseract, which is not natively compatible with JAX's automatic differentiation. So we need to wrap the Tesseract in a JAX-compatible function that can be differentiated, by registering a JAX custom derivative for the Tesseract's `apply` method, via [`jax.pure_callback`](https://docs.jax.dev/en/latest/_autosummary/jax.pure_callback.html) and [`jax.custom_vjp`](https://docs.jax.dev/en/latest/_autosummary/jax.custom_vjp.html).\n",
+    "\n",
+    "<div class=admonition note alert alert-warning>\n",
+    "<p class=admonition-title>Note</p>\n",
+    "\n",
+    "While this works in this example, registering Tesseracts by hand is not the recommended way to use Tesseracts in JAX. Instead, you should check out [Tesseract-JAX](https://github.com/pasteurlabs/tesseract-jax), which automatically registers Tesseracts as JAX-compatible functions, allowing you to use them seamlessly in JAX code without needing to manually define callbacks or register custom derivatives.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Any\n",
+    "\n",
+    "# Construct the forward operator using tesseract to integrate the system until next observation is available\n",
+    "params_M = {\n",
+    "    \"F\": 18.0,\n",
+    "    \"dt\": 0.005,\n",
+    "    \"n_steps\": obs_gap,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def convert_jax_to_python(inputs_dict: dict[str, Any]) -> dict[str, Any]:\n",
+    "    \"\"\"Convert JAX arrays back to Python types for tesseract API.\n",
+    "\n",
+    "    Args:\n",
+    "        inputs_dict: Dictionary potentially containing JAX arrays.\n",
+    "\n",
+    "    Returns:\n",
+    "        Dictionary with JAX scalars converted to Python types while preserving\n",
+    "        JAX arrays for state variables.\n",
+    "    \"\"\"\n",
+    "    converted = {}\n",
+    "    for key, value in inputs_dict.items():\n",
+    "        if key == \"state\":\n",
+    "            converted[key] = value  # Keep state as JAX array\n",
+    "        elif hasattr(value, \"item\"):\n",
+    "            converted[key] = value.item()  # Convert JAX scalars to Python types\n",
+    "        else:\n",
+    "            converted[key] = value\n",
+    "    return converted\n",
+    "\n",
+    "\n",
+    "@jax.custom_vjp\n",
+    "def M(x: jnp.ndarray) -> dict[str, jnp.ndarray]:\n",
+    "    \"\"\"Forward operator integrating Lorenz 96 for n_steps.\n",
+    "\n",
+    "    Args:\n",
+    "        x: Initial state vector of shape (8,).\n",
+    "\n",
+    "    Returns:\n",
+    "        Dictionary containing trajectory of shape (n_steps, 8) under 'result' key.\n",
+    "    \"\"\"\n",
+    "    n_steps = params_M[\"n_steps\"]\n",
+    "    result = jax.pure_callback(\n",
+    "        lambda inputs: lorenz_tesseract.apply(inputs=convert_jax_to_python(inputs)),\n",
+    "        {\"result\": jax.ShapeDtypeStruct((n_steps, x.shape[0]), x.dtype)},\n",
+    "        dict(state=x, **params_M),\n",
+    "    )\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "def M_fwd(x: jnp.ndarray) -> tuple[dict[str, jnp.ndarray], jnp.ndarray]:\n",
+    "    \"\"\"Forward pass for custom VJP returning result and residuals.\n",
+    "\n",
+    "    Args:\n",
+    "        x: Initial state vector of shape (8,).\n",
+    "\n",
+    "    Returns:\n",
+    "        Tuple containing:\n",
+    "            - Dictionary with trajectory under 'result' key\n",
+    "            - Residuals (initial state) for backward pass\n",
+    "    \"\"\"\n",
+    "    return M(x), x\n",
+    "\n",
+    "\n",
+    "def M_bwd(\n",
+    "    residuals: jnp.ndarray, cotangents: dict[str, jnp.ndarray]\n",
+    ") -> tuple[jnp.ndarray]:\n",
+    "    \"\"\"Backward pass computing vector-Jacobian product.\n",
+    "\n",
+    "    Args:\n",
+    "        residuals: Stored initial state from forward pass.\n",
+    "        cotangents: Gradient vector with same structure as forward output.\n",
+    "\n",
+    "    Returns:\n",
+    "        Tuple containing gradient with respect to initial state of shape (8,).\n",
+    "    \"\"\"\n",
+    "    x = residuals\n",
+    "    cotangents_result = cotangents[\"result\"]\n",
+    "\n",
+    "    grad_x = jax.pure_callback(\n",
+    "        lambda inputs, cot_vec: lorenz_tesseract.vector_jacobian_product(\n",
+    "            inputs=convert_jax_to_python(inputs),\n",
+    "            vjp_inputs=[\"state\"],\n",
+    "            vjp_outputs=[\"result\"],\n",
+    "            cotangent_vector=cot_vec,\n",
+    "        ),\n",
+    "        jax.ShapeDtypeStruct(x.shape, x.dtype),\n",
+    "        dict(state=x, **params_M),\n",
+    "        {\"result\": cotangents_result},\n",
+    "    )\n",
+    "    return (grad_x,)\n",
+    "\n",
+    "\n",
+    "M.defvjp(M_fwd, M_bwd)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Application of the 4D-Var method to the Lorenz 96 model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# integrate to first observation\n",
+    "history = []\n",
+    "X_pred = jnp.zeros((obs_gap + 1, X0.shape[0]))\n",
+    "X_pred = X_pred.at[0].set(X0)\n",
+    "Xf = M(X0)[\"result\"]\n",
+    "X_pred = X_pred.at[1:].set(Xf)\n",
+    "Xb = Xf[-1]\n",
+    "history.append(X_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we run the cyclic forecast-assimilation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Now assimilating observations: [[ 2.799733    9.399986    0.46893954 -2.4860692   1.3382943  12.572049\n",
+      "   3.3946197  -3.8351834 ]\n",
+      " [ 3.379249    8.853455    2.9171748   0.03306353  3.867188   11.950285\n",
+      "  -0.8432639   3.8301866 ]\n",
+      " [11.4476185   0.6421503  -1.4374818   1.4826026   9.486775    7.7398486\n",
+      "  -1.7716159   5.5438924 ]]\n",
+      "Forecasting next assimilation window...\n",
+      "Now assimilating observations: [[10.179231   -0.24670759  5.3289804  10.904022    4.2516203  -5.7708306\n",
+      "   0.8604067   5.689573  ]\n",
+      " [ 5.4109397  -1.3887025   5.18376     8.259685   -3.789405    4.848869\n",
+      "   6.546297    8.800019  ]\n",
+      " [ 1.5806762   1.6670504   7.6248302   6.626485   -0.29925     5.1062775\n",
+      "  11.641597   -5.082076  ]]\n",
+      "Forecasting next assimilation window...\n",
+      "Now assimilating observations: [[ 5.8376927 12.217752   0.9033669 -0.9313403  1.507611   8.427427\n",
+      "   6.484769  -1.1838636]\n",
+      " [ 5.5618935 10.293968  -1.5365992  4.548709   9.417823   4.735697\n",
+      "  -3.5575433  1.5427291]\n",
+      " [ 9.448657   2.7177422 -1.634881   6.2142787  8.478294  -3.347425\n",
+      "   4.947667   6.5599914]]\n",
+      "Forecasting next assimilation window...\n"
+     ]
+    }
+   ],
+   "source": [
+    "for obs_traj in obs_trajs[:3]:\n",
+    "    print(\"Now assimilating observations:\", obs_traj)\n",
+    "    X0 = analysis_4DVar(Xb, obs_traj, M, H, R_inv, B_inv, n_obs=n_obs)\n",
+    "\n",
+    "    # forecast step\n",
+    "    print(\"Forecasting next assimilation window...\")\n",
+    "    X_pred = jnp.zeros((n_obs * obs_gap + 1, X0.shape[0]))\n",
+    "    X_pred = X_pred.at[0].set(X0)\n",
+    "    Xf = M_window(X0)[\"result\"]\n",
+    "    X_pred = X_pred.at[1:].set(Xf)\n",
+    "    Xb = Xf[-1]\n",
+    "\n",
+    "    history.append(X_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_da_res(forecast_list: list) -> jnp.ndarray:\n",
+    "    \"\"\"Get the DA results from a list of forecasts.\n",
+    "\n",
+    "    Args:\n",
+    "        forecast_list: List of forecasts.\n",
+    "\n",
+    "    Output:\n",
+    "        concatenated_forecasts: Concatenated forecasts.\n",
+    "    \"\"\"\n",
+    "    processed_forecasts = []\n",
+    "\n",
+    "    for forecasts in forecast_list:\n",
+    "        # Remove the last time step from each trajectory\n",
+    "        trimmed_forecasts = forecasts[:-1, :]  # Shape (Ne, Nt, nx)\n",
+    "        processed_forecasts.append(trimmed_forecasts)\n",
+    "\n",
+    "    # Concatenate all processed forecasts along the time dimension\n",
+    "    concatenated_forecasts = jnp.concatenate(processed_forecasts, axis=0)\n",
+    "\n",
+    "    return concatenated_forecasts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x4DVar = get_da_res(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x15ef7f7a0>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the results\n",
+    "pos = 0\n",
+    "plot_end = 500\n",
+    "plot_obs_end = plot_end // obs_gap\n",
+    "plt.figure(figsize=(10, 4))\n",
+    "plt.plot(x4DVar[:plot_end, pos], label=f\"4DVar $X_{pos}$\", color=\".2\")\n",
+    "plt.plot(pred[:plot_end, pos], label=f\"Surrogate $X_{pos}$\")\n",
+    "plt.plot(true_trajectory[:plot_end, pos], label=f\"True $X_{pos}$\")\n",
+    "plt.plot(time_obs[:plot_obs_end], obs[:plot_obs_end, pos], \"o\", label=\"Observations\")\n",
+    "plt.legend(loc=\"upper left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The black curve, after running 4D-Var, hugs the orange “true” line much more closely. You can see at steps 50, 200, and 350 (where we perform 4D-Var with following 3 observations) that analysis pulls the model back toward the true state, even though we're not even modelling the same system that the true data was generated from. Data assimilation keeps us on track for far longer than just letting the imperfect model run free."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tear down Tesseract after use to prevent resource leaks\n",
+    "lorenz_tesseract.teardown()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## And that's it! \n",
+    "\n",
+    "We have implemented our Lorenz-96 solver in **Tesseract** using **JAX**, which gives us full separation of concerns and automatic differentiation. This lets us directly compute gradients of the 4D-Var cost function and perform gradient-based optimization end-to-end, without any manual gradient coding.\n",
+    "\n",
+    "So we can better appreciate the advantages of built-in automatic differentiation, here's how operational weather centers tackle 4D-Var data assimilation.\n",
+    "\n",
+    "**Traditional numerical weather prediction (mostly Fortran, no autodiff)** ([example](https://www.ecmwf.int/en/newsletter/175/earth-system-science/linearised-physics-heart-ecmwfs-4d-var)):\n",
+    "\n",
+    "- Requires to manually derive and implement tangent-linear approximation and adjoint routines for every physical processes (radiation, convection, orographic drag, etc.) for gradient computing.  \n",
+    "- Writing, testing and maintaining those adjoints is a massive, decades-long effort—and it never really ends.  \n",
+    "- Every time you tweak or add a new physical processes, you have to derive and code its tangent-linear/adjoint from scratch.  \n",
+    "- Subtle mismatches between forward and adjoint code can sneak in.\n",
+    "\n",
+    "Now contrast this with our approach here.\n",
+    "\n",
+    "**Differentiable model with JAX and Tesseract**:\n",
+    "\n",
+    "- Gradients are automatic -- JAX traces the whole nonlinear model, so we don’t write a single tangent-linear model/adjoint by hand.  \n",
+    "- We hook directly into familiar ML optimizers (SGD, Adam, etc.), making experimentation quick and painless.  \n",
+    "- Adding new physics or parameterizations is as simple as dropping them into the code -- JAX takes care of all derivatives.  \n",
+    "- One code path runs forward and backward, so analyses stay consistent and debugging is far easier.  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "science",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/data-assimilation-4dvar/lorenz96_two_scale_F_18_sample_0_small.npz b/demo/data-assimilation-4dvar/lorenz96_two_scale_F_18_sample_0_small.npz
new file mode 100644
index 00000000..240ad894
Binary files /dev/null and b/demo/data-assimilation-4dvar/lorenz96_two_scale_F_18_sample_0_small.npz differ
diff --git a/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_api.py b/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_api.py
new file mode 100644
index 00000000..4feb588b
--- /dev/null
+++ b/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_api.py
@@ -0,0 +1,191 @@
+# Copyright 2025 Pasteur Labs. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Tesseract API module for lorenz
+# Generated by tesseract 0.8.5 on 2025-04-30T09:44:04.234338
+
+from typing import Any
+
+import equinox as eqx
+import jax
+import jax.numpy as jnp
+from pydantic import BaseModel, Field
+
+from tesseract_core.runtime import Array, Differentiable, Float32
+from tesseract_core.runtime.tree_transforms import filter_func, flatten_with_paths
+
+
+class InputSchema(BaseModel):
+    """Input schema for forecasting of Lorenz 96 system."""
+
+    state: Differentiable[Array[(None,), Float32]] = Field(
+        description="A state vector for the Lorenz 96 system"
+    )
+    F: float = Field(description="Forcing parameter for Lorenz 96", default=8.0)
+    dt: float = Field(description="Time step for integration", default=0.05)
+    n_steps: int = Field(description="Number of integration steps", default=1)
+
+
+class OutputSchema(BaseModel):
+    """Output schema for forecasting of Lorenz 96 system."""
+
+    result: Differentiable[Array[(None, None), Float32]] = Field(
+        description="A trajectorie of predictions after integration"
+    )
+
+
+def lorenz96_step(state: jnp.ndarray, F: float, dt: float) -> jnp.ndarray:
+    """Perform one step of RK4 integration for the Lorenz 96 system."""
+
+    def lorenz96_derivatives(x: jnp.ndarray) -> jnp.ndarray:
+        """Compute the derivatives for Lorenz 96 system."""
+        N = x.shape[0]
+        # Create arrays for indices with wraparound
+        ip1 = (jnp.arange(N) + 1) % N  # i+1 with wraparound
+        im1 = (jnp.arange(N) - 1) % N  # i-1 with wraparound
+        im2 = (jnp.arange(N) - 2) % N  # i-2 with wraparound
+        # Compute derivatives: dx_i/dt = (x_{i+1} - x_{i-2}) * x_{i-1} - x_i + F
+        d = (x[ip1] - x[im2]) * x[im1] - x + F
+        return d
+
+    # RK4 integration
+    k1 = lorenz96_derivatives(state)
+    k2 = lorenz96_derivatives(state + dt * k1 / 2)
+    k3 = lorenz96_derivatives(state + dt * k2 / 2)
+    k4 = lorenz96_derivatives(state + dt * k3)
+    return state + dt * (k1 + 2 * k2 + 2 * k3 + k4) / 6
+
+
+def lorenz96_multi_step(
+    state: jnp.ndarray, F: float, dt: float, n_steps: int
+) -> jnp.ndarray:
+    """Perform multiple steps of Lorenz 96 integration using scan."""
+
+    def step_fn(state: jnp.ndarray, _: Any) -> tuple:
+        return lorenz96_step(state, F, dt), state
+
+    _, trajectory = jax.lax.scan(step_fn, state, None, length=n_steps)
+    return trajectory
+
+
+@eqx.filter_jit
+def apply_jit(inputs: dict) -> dict:
+    """The apply_jit function for the Lorenz 96 tesseract."""
+    trajectory = lorenz96_multi_step(**inputs)
+    return dict(result=trajectory)
+
+
+def apply(inputs: InputSchema) -> OutputSchema:
+    """The apply function for the Lorenz 96 tesseract."""
+    return apply_jit(inputs.model_dump())
+
+
+def abstract_eval(abstract_inputs: Any) -> Any:
+    """Calculate output shape of apply from the shape of its inputs."""
+    is_shapedtype_dict = lambda x: type(x) is dict and (x.keys() == {"shape", "dtype"})
+    is_shapedtype_struct = lambda x: isinstance(x, jax.ShapeDtypeStruct)
+
+    jaxified_inputs = jax.tree.map(
+        lambda x: jax.ShapeDtypeStruct(**x) if is_shapedtype_dict(x) else x,
+        abstract_inputs.model_dump(),
+        is_leaf=is_shapedtype_dict,
+    )
+    dynamic_inputs, static_inputs = eqx.partition(
+        jaxified_inputs, filter_spec=is_shapedtype_struct
+    )
+
+    def wrapped_apply(dynamic_inputs: Any) -> Any:
+        inputs = eqx.combine(static_inputs, dynamic_inputs)
+        return apply_jit(inputs)
+
+    jax_shapes = jax.eval_shape(wrapped_apply, dynamic_inputs)
+    return jax.tree.map(
+        lambda x: (
+            {"shape": x.shape, "dtype": str(x.dtype)} if is_shapedtype_struct(x) else x
+        ),
+        jax_shapes,
+        is_leaf=is_shapedtype_struct,
+    )
+
+
+@eqx.filter_jit
+def jac_jit(
+    inputs: dict,
+    jac_inputs: tuple[str],
+    jac_outputs: tuple[str],
+) -> dict:
+    """The jac_jit function for the Lorenz 96 tesseract."""
+    filtered_apply = filter_func(apply_jit, inputs, jac_outputs)
+    return jax.jacrev(filtered_apply)(
+        flatten_with_paths(inputs, include_paths=jac_inputs)
+    )
+
+
+def jacobian(
+    inputs: InputSchema,
+    jac_inputs: set[str],
+    jac_outputs: set[str],
+) -> Any:
+    """The jacobian function for the Lorenz 96 tesseract."""
+    return jac_jit(inputs.model_dump(), tuple(jac_inputs), tuple(jac_outputs))
+
+
+@eqx.filter_jit
+def jvp_jit(
+    inputs: dict,
+    jvp_inputs: tuple[str],
+    jvp_outputs: tuple[str],
+    tangent_vector: dict,
+) -> Any:
+    """The jvp_jit function for the Lorenz 96 tesseract."""
+    filtered_apply = filter_func(apply_jit, inputs, jvp_outputs)
+    return jax.jvp(
+        filtered_apply,
+        [flatten_with_paths(inputs, include_paths=jvp_inputs)],
+        [tangent_vector],
+    )[1]
+
+
+def jacobian_vector_product(
+    inputs: InputSchema,
+    jvp_inputs: set[str],
+    jvp_outputs: set[str],
+    tangent_vector: dict[str, Any],
+) -> Any:
+    """The jacobian vector product function for the Lorenz 96 tesseract."""
+    return jvp_jit(
+        inputs.model_dump(),
+        tuple(jvp_inputs),
+        tuple(jvp_outputs),
+        tangent_vector,
+    )
+
+
+@eqx.filter_jit
+def vjp_jit(
+    inputs: dict,
+    vjp_inputs: tuple[str],
+    vjp_outputs: tuple[str],
+    cotangent_vector: dict,
+) -> Any:
+    """The vjp_jit function for the Lorenz 96 tesseract."""
+    filtered_apply = filter_func(apply_jit, inputs, vjp_outputs)
+    _, vjp_func = jax.vjp(
+        filtered_apply, flatten_with_paths(inputs, include_paths=vjp_inputs)
+    )
+    return vjp_func(cotangent_vector)[0]
+
+
+def vector_jacobian_product(
+    inputs: InputSchema,
+    vjp_inputs: set[str],
+    vjp_outputs: set[str],
+    cotangent_vector: dict[str, Any],
+) -> Any:
+    """The vector jacobian product function for the Lorenz 96 tesseract."""
+    return vjp_jit(
+        inputs.model_dump(),
+        tuple(vjp_inputs),
+        tuple(vjp_outputs),
+        cotangent_vector,
+    )
diff --git a/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_config.yaml b/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_config.yaml
new file mode 100644
index 00000000..4f0f3ff2
--- /dev/null
+++ b/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_config.yaml
@@ -0,0 +1,27 @@
+# Tesseract configuration file
+# Generated by tesseract 0.8.5 on 2025-04-30T09:44:04.234338
+
+name: "lorenz"
+version: "0+unknown"
+description: "Tesseract that runs a scan of forecasting for a single-scale Lorenz 96 system"
+
+build_config:
+  # Base image to use for the container, must be Ubuntu or Debian-based
+  # base_image: "debian:bookworm-slim"
+
+  # Platform to build the container for. In general, images can only be executed
+  # on the platform they were built for.
+  target_platform: "native"
+
+  # Additional packages to install in the container (via apt-get)
+  # extra_packages:
+  #  - package_name
+
+  # Data to copy into the container, relative to the project root
+  # package_data:
+  #  - [path/to/source, path/to/destination]
+
+  # Additional Dockerfile commands to run during the build process
+  # custom_build_steps:
+  #  - |
+  #   RUN echo "Hello, World!"
diff --git a/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_requirements.txt b/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_requirements.txt
new file mode 100644
index 00000000..68ee1b51
--- /dev/null
+++ b/demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_requirements.txt
@@ -0,0 +1,10 @@
+# Tesseract requirements file
+# Generated by tesseract 0.8.5 on 2025-04-30T09:44:04.234338
+
+# Add Python requirements like this:
+numpy==1.26.0
+jax[cpu]==0.5.2
+equinox
+# This may contain private dependencies via SSH URLs:
+# git+ssh://git@github.com/username/repo.git@branch
+# (use `tesseract build --forward-ssh-agent` to grant the builder access to your SSH keys)
diff --git a/demo/data-assimilation-4dvar/requirements.txt b/demo/data-assimilation-4dvar/requirements.txt
new file mode 100644
index 00000000..b93dbb43
--- /dev/null
+++ b/demo/data-assimilation-4dvar/requirements.txt
@@ -0,0 +1,4 @@
+jax[cpu]
+numpy
+scipy
+matplotlib
diff --git a/docs/conf.py b/docs/conf.py
index 36796469..06892f39 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -11,6 +11,8 @@
 
 import os
 import re
+import shutil
+from pathlib import Path
 
 from tesseract_core import __version__
 
@@ -40,7 +42,7 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    "myst_parser",
+    "myst_nb",
     "sphinx.ext.intersphinx",
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",
@@ -106,3 +108,15 @@ def setup(app) -> None:
     """Sphinx setup function. Used to register custom stuff."""
     # HACK: We zip the examples folder here so that it can be downloaded
     zip_examples_folder()
+
+
+# -- Handle Jupyter notebooks ------------------------------------------------
+
+# Do not execute notebooks during build (just take existing output)
+nb_execution_mode = "off"
+
+# Copy example notebooks to auto_examples folder on every build
+for example_notebook in Path("../demo").glob("*/demo.ipynb"):
+    # Copy the example notebook to the docs folder
+    dest = (Path("content/demo") / example_notebook.parent.name).with_suffix(".ipynb")
+    shutil.copyfile(example_notebook, dest)
diff --git a/docs/content/demo/.gitignore b/docs/content/demo/.gitignore
new file mode 100644
index 00000000..7161280e
--- /dev/null
+++ b/docs/content/demo/.gitignore
@@ -0,0 +1,2 @@
+# Notebooks are copied on demand by conf.py
+*.ipynb
diff --git a/docs/content/demo/demo.md b/docs/content/demo/demo.md
new file mode 100644
index 00000000..4c4b6125
--- /dev/null
+++ b/docs/content/demo/demo.md
@@ -0,0 +1,26 @@
+# Demo: 4D-variational data assimilation with Tesseract
+
+```{toctree}
+:caption: Contents
+:maxdepth: 1
+:hidden:
+
+data-assimilation-4dvar.ipynb
+lorenz_tesseract.md
+```
+
+(cards-clickable)=
+
+
+:::{card} 4D-Variational Data Assimilation for a Chaotic Dynamical System
+:link: data-assimilation-4dvar.html
+
+Demo using differentiable Tesseracts to implement a 4D-Var data assimilation scheme
+for a chaotic dynamical system
+:::
+
+:::{card} Lorenz Tesseract
+:link: lorenz_tesseract.html
+
+Detailed implementation of the Lorenz Tesseract used in the 4D-Var demo.
+:::
diff --git a/docs/content/demo/lorenz_tesseract.md b/docs/content/demo/lorenz_tesseract.md
new file mode 100644
index 00000000..217d9f4c
--- /dev/null
+++ b/docs/content/demo/lorenz_tesseract.md
@@ -0,0 +1,23 @@
+# Building the JAX Solver Tesseract for Lorenz-96
+
+This examples demonstrates how the JAX solver Tesseract for the Lorenz-96 model is built for the purposes of the data assimilation demo.
+
+Below is the input and output schema definition for the Lorenz Tesseract.
+
+```{literalinclude} ../../../demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_api.py
+:language: python
+:pyobject: InputSchema
+```
+
+```{literalinclude} ../../../demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_api.py
+:language: python
+:pyobject: OutputSchema
+```
+
+Below is the implementation of the apply function, which takes in an initial condition and returns a trajectory of physical states.
+
+```{literalinclude} ../../../demo/data-assimilation-4dvar/lorenz_tesseract/tesseract_api.py
+:language: python
+:start-at: def lorenz96_step
+:end-before:     return apply_jit(inputs.model_dump())
+```
diff --git a/docs/content/examples/arm64.md b/docs/content/examples/building-blocks/arm64.md
similarity index 90%
rename from docs/content/examples/arm64.md
rename to docs/content/examples/building-blocks/arm64.md
index e541bf3e..26a05305 100644
--- a/docs/content/examples/arm64.md
+++ b/docs/content/examples/building-blocks/arm64.md
@@ -1,4 +1,4 @@
-# Custom build steps: PyVista on ARM64 example
+# Custom build steps: PyVista on ARM64
 
 ## Context
 
@@ -8,7 +8,7 @@ Tesseracts use by default an official Python docker image as the base image. Alt
 Via `tesseract_config.yaml`, it is possible to somewhat flexibly alter the build process to accomodate different needs. As a concrete example, here's what we had to do internally in order to to build an arm64 Tesseract with PyVista installed as a dependency:
 
 
-```{literalinclude} ../../../examples/pyvista-arm64/tesseract_config.yaml
+```{literalinclude} ../../../../examples/pyvista-arm64/tesseract_config.yaml
 :language: yaml
 ```
 
diff --git a/docs/content/examples/dataloader.md b/docs/content/examples/building-blocks/dataloader.md
similarity index 52%
rename from docs/content/examples/dataloader.md
rename to docs/content/examples/building-blocks/dataloader.md
index 61d4481b..d4d25f81 100644
--- a/docs/content/examples/dataloader.md
+++ b/docs/content/examples/building-blocks/dataloader.md
@@ -1,27 +1,27 @@
-# Dataloader
+# Out-of-core data loading
 
 ## Context
 This is an example of a Tesseract that loads in data from a folder by mounting the folder in the cli.
 
-## Example Tesseract (examples/dataloader)
+## Example Tesseract (`examples/dataloader`)
 
-In the actual Tesseract, we may have logic that's expecting data samples for the input schema
+In the actual Tesseract, we may have logic that's expecting data samples for the input schema:
 
-```{literalinclude} ../../../examples/dataloader/tesseract_api.py
+```{literalinclude} ../../../../examples/dataloader/tesseract_api.py
 :pyobject: InputSchema
 :language: python
 ```
 
-The inputted data may be processed as such in an apply function
+The inputted data may be processed as such in an apply function:
 
-```{literalinclude} ../../../examples/dataloader/tesseract_api.py
+```{literalinclude} ../../../../examples/dataloader/tesseract_api.py
 :pyobject: apply
 :language: python
 ```
 
-You can then pass in data into this Tesseract by mounting the directory where the data samples are stored using the tesseract flag `--volume`
+You can then pass in data into this Tesseract by mounting the directory where the data samples are stored using the tesseract flag `--volume`:
 
-```{literalinclude} ../../../examples/dataloader/test-tesseract.sh
+```{literalinclude} ../../../../examples/dataloader/test-tesseract.sh
 :lines: 10-12
 :language: bash
 ```
diff --git a/docs/content/examples/building-blocks/helloworld.md b/docs/content/examples/building-blocks/helloworld.md
new file mode 100644
index 00000000..2ec83263
--- /dev/null
+++ b/docs/content/examples/building-blocks/helloworld.md
@@ -0,0 +1,11 @@
+# HelloWorld
+
+## Context
+This is a basic Hello World Tesseract. See [getting-started]
+for more details on this Tesseract.
+
+## Example Tesseract (`examples/helloworld`)
+
+```{literalinclude} ../../../../examples/helloworld/tesseract_api.py
+:language: python
+```
diff --git a/docs/content/examples/localdependency.md b/docs/content/examples/building-blocks/localdependency.md
similarity index 94%
rename from docs/content/examples/localdependency.md
rename to docs/content/examples/building-blocks/localdependency.md
index c79a21c5..452e3336 100644
--- a/docs/content/examples/localdependency.md
+++ b/docs/content/examples/building-blocks/localdependency.md
@@ -18,13 +18,13 @@ $ tesseract init --name cowsay
 
  And let's then edit the `tesseract_api.py` file to read
 
-```{literalinclude} ../../../examples/conda/tesseract_api.py
+```{literalinclude} ../../../../examples/conda/tesseract_api.py
 :language: python
 ```
 
 
 This Tesseract will accept a message like "Hello, world!" as an input and return
-```{literalinclude} ../../../examples/conda/expected_output.txt
+```{literalinclude} ../../../../examples/conda/expected_output.txt
 :language: text
 ```
 
diff --git a/docs/content/examples/packagedata.md b/docs/content/examples/building-blocks/packagedata.md
similarity index 79%
rename from docs/content/examples/packagedata.md
rename to docs/content/examples/building-blocks/packagedata.md
index e4b06001..86976d9a 100644
--- a/docs/content/examples/packagedata.md
+++ b/docs/content/examples/building-blocks/packagedata.md
@@ -1,4 +1,4 @@
-# Passing in local data into a Tesseract
+# Packaging local files into a Tesseract
 
 ## Context
 You may need to include local files into your Tesseract, e.g. if we want to load in pretrained model weights, or artifacts that we may need access to. This guide will demonstrate how.
@@ -10,14 +10,14 @@ In this example, we want to include and process `parameters.json` file in our Te
 
 In order to achieve this, we must modify the `tesseract_config.yaml` file with the source (local path) and the target destinations (location in the Tesseract container).
 
-```{literalinclude} ../../../examples/package_data/tesseract_config.yaml
+```{literalinclude} ../../../../examples/package_data/tesseract_config.yaml
 :language: yaml
 :start-at: build_config:
 ```
 
 Now that it is included in the build config, we can use it in our Tesseracts apply function as normal.
 
-```{literalinclude} ../../../examples/package_data/tesseract_api.py
+```{literalinclude} ../../../../examples/package_data/tesseract_api.py
 :language: python
 :start-at: def apply(inputs: InputSchema) -> OutputSchema:
 ```
diff --git a/docs/content/examples/vectoradd.md b/docs/content/examples/building-blocks/vectoradd.md
similarity index 72%
rename from docs/content/examples/vectoradd.md
rename to docs/content/examples/building-blocks/vectoradd.md
index 981e7ce2..e5a2d7a8 100644
--- a/docs/content/examples/vectoradd.md
+++ b/docs/content/examples/building-blocks/vectoradd.md
@@ -1,4 +1,4 @@
-# Vector Add
+# VectorAdd
 
 ## Context
 Example using vector add with differentiable inputs and jacobians.
@@ -10,12 +10,12 @@ Inputs must be Differentiable if we wish to write a Jacobian function for the Te
 
 We also have an example of a basic apply function and how it utilizes the inputs.
 
-```{literalinclude} ../../../examples/vectoradd/tesseract_api.py
+```{literalinclude} ../../../../examples/vectoradd/tesseract_api.py
 :pyobject: InputSchema
 :language: python
 ```
 
-```{literalinclude} ../../../examples/vectoradd/tesseract_api.py
+```{literalinclude} ../../../../examples/vectoradd/tesseract_api.py
 :pyobject: apply
 :language: python
 ```
@@ -27,12 +27,12 @@ a Jacobian function.
 
 In order for us to do that, we must also write the abstract eval function.
 
-```{literalinclude} ../../../examples/vectoradd/tesseract_api.py
+```{literalinclude} ../../../../examples/vectoradd/tesseract_api.py
 :pyobject: abstract_eval
 :language: python
 ```
 
-```{literalinclude} ../../../examples/vectoradd/tesseract_api.py
+```{literalinclude} ../../../../examples/vectoradd/tesseract_api.py
 :pyobject: jacobian
 :language: python
 ```
diff --git a/docs/content/examples/example_gallery.md b/docs/content/examples/example_gallery.md
index 7269cbf6..f246fd88 100644
--- a/docs/content/examples/example_gallery.md
+++ b/docs/content/examples/example_gallery.md
@@ -1,53 +1,60 @@
-Example Gallery
-====================
+# Building Blocks
 
 ```{toctree}
 :name: example-gallery
 :caption: Example Gallery
 :maxdepth: 2
 :hidden:
+:glob:
+building-blocks/helloworld.md
+building-blocks/vectoradd.md
+building-blocks/packagedata.md
+building-blocks/arm64.md
+building-blocks/localdependency.md
+building-blocks/dataloader.md
+```
+
+This is a gallery of Tesseract examples that end at the `build` stage of the Tesseract lifecycle, and that can act as starting points to define and build your own Tesseracts.
+
+You can also find these Tesseracts in the `examples` directory of the [code repository](https://github.com/pasteurlabs/tesseract-core).
 
-arm64
-localdependency
-packagedata
-helloworld
-dataloader
-vectoradd
+```{important}
+**Beyond the Build**: The real magic happens long after building a Tesseract. For some example applications that *use* Tesseracts in workflows, check out the [Demo](../demo/demo.md) and [Community Showcase](https://si-tesseract.discourse.group/c/showcase/11).
 ```
 
 
 ::::{grid} 2
    :gutter: 2
 
-   :::{grid-item-card} Hello World
-      :link: helloworld.html
+   :::{grid-item-card} HelloWorld
+      :link: building-blocks/helloworld.html
 
-      A simple hello world Tesseract.
+      A simple "hello world" Tesseract.
    :::
-   :::{grid-item-card} Package Data
-      :link: packagedata.html
+   :::{grid-item-card} VectorAdd
+      :link: building-blocks/vectoradd.html
 
-      A guide on including local data into Tesseract package.
+     Tesseract performing vector addition. Highlighting simple array operations and how to use the Tesseract Python API.
    :::
-   :::{grid-item-card} Vector Add
-      :link: vectoradd.html
+   :::{grid-item-card} Package Data
+      :link: building-blocks/packagedata.html
 
-     Tesseract doing vector add.
+      A guide on including local files into a built Tesseract.
    :::
-   :::{grid-item-card} Pyvista on Arm64
-      :link: arm64.html
+   :::{grid-item-card} Pyvista on ARM64
+      :link: building-blocks/arm64.html
 
-      A guide on how to set up `tesseract_config` for pyvista on arm64.
+      A guide showcasing how to use custom build steps to install pyvista within an ARM64 Tesseract.
    :::
    :::{grid-item-card} Local Dependencies
-      :link: localdependency.html
+      :link: building-blocks/localdependency.html
 
-      A guide on installing local dependencies (`cowsay`) into a Tesseract.
+      A guide on installing local Python packages into a Tesseract.
    :::
    :::{grid-item-card} Data Loader
-      :link: dataloader.html
+      :link: building-blocks/dataloader.html
 
-     Tesseract that loads in data samples from a folder.
+     Tesseract that loads in data samples from a folder without loading them into memory.
    :::
 
 ::::
diff --git a/docs/content/examples/helloworld.md b/docs/content/examples/helloworld.md
deleted file mode 100644
index 145b9792..00000000
--- a/docs/content/examples/helloworld.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Hello World
-
-## Context
-This is a basic Hello World Tesseract. See: [#Get Started](#getting-started)
-for more details on this Tesseract.
-
-## Example Tesseract (examples/helloworld)
-
-
-```{literalinclude} ../../../examples/helloworld/tesseract_api.py
-:language: python
-```
diff --git a/docs/index.md b/docs/index.md
index 982c2a10..1e77bbc5 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -95,12 +95,13 @@ content/using-tesseracts/advanced.md
 ```
 
 ```{toctree}
-:caption: Tutorials
+:caption: Examples
 :maxdepth: 2
 :hidden:
 
-Expert showcase <https://si-tesseract.discourse.group/c/showcase/expert-showcase/12>
-content/examples/example_gallery
+content/examples/example_gallery.md
+content/demo/demo.md
+Tesseract Community Showcase <https://si-tesseract.discourse.group/c/showcase/11>
 ```
 
 ```{toctree}
diff --git a/examples/README.md b/examples/README.md
index 6dcb495b..ae7bdce5 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -2,4 +2,4 @@
 
 This folder contains some basic examples of Tesseract usage, tested regularly to ensure they work with the latest version of Tesseract.
 
-Looking for more advanced tutorials? Check out our [official tutorials](https://si-tesseract.discourse.group/c/showcase/tutorials/12) and [how-to guides](https://si-tesseract.discourse.group/c/support/how-to-guides/10).
+For more information, see the [Tesseract documentation](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/).
diff --git a/inject_runtime_pyproject.py b/inject_runtime_pyproject.py
index 6626c280..4e82a9bb 100644
--- a/inject_runtime_pyproject.py
+++ b/inject_runtime_pyproject.py
@@ -15,7 +15,7 @@
         "sphinx",
         "sphinx_autodoc_typehints",
         "furo",
-        "myst-parser",
+        "myst-nb",
         "sphinx_click",
         "autodoc_pydantic",
         "sphinx_design",
diff --git a/production.uv.lock b/production.uv.lock
index 1714bebb..74a02985 100644
--- a/production.uv.lock
+++ b/production.uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 1
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.11'",
@@ -8,7 +8,7 @@ resolution-markers = [
 
 [[package]]
 name = "aiobotocore"
-version = "2.22.0"
+version = "2.21.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -19,23 +19,23 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
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diff --git a/requirements.txt b/requirements.txt
index 3e218a43..b831c62e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,9 +2,9 @@
 #    uv export --frozen --color never --no-emit-project --no-hashes
 annotated-types==0.7.0
     # via pydantic
-certifi==2025.4.26
+certifi==2025.1.31
     # via requests
-charset-normalizer==3.4.2
+charset-normalizer==3.4.1
     # via requests
 click==8.1.8
     # via typer
@@ -22,7 +22,7 @@ mdurl==0.1.2
     # via markdown-it-py
 numpy==2.2.4
     # via tesseract-core
-pip==25.1.1
+pip==25.0.1
     # via tesseract-core
 pydantic==2.11.0
     # via tesseract-core
@@ -42,14 +42,14 @@ shellingham==1.5.4
     # via typer
 typer==0.15.2
     # via tesseract-core
-typing-extensions==4.13.2
+typing-extensions==4.13.0
     # via
     #   pydantic
     #   pydantic-core
     #   rich
     #   typer
     #   typing-inspection
-typing-inspection==0.4.1
+typing-inspection==0.4.0
     # via pydantic
-urllib3==2.4.0
+urllib3==2.3.0
     # via requests