{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Learned Closure: Training a Neural Viscosity Model Through a PDE Solver (PyTorch)\n", "\n", "In this tutorial, you will learn how to:\n", "\n", "1. **Build a Tesseract** that wraps a single-timestep differentiable PDE solver (a 1D Burgers' equation solver) and exposes a vector-Jacobian product.\n", "2. **Use [Tesseract-Torch](https://github.com/pasteurlabs/tesseract-torch)** to call the served solver as a native PyTorch autograd layer via `apply_tesseract`, so gradients flow through the container automatically.\n", "3. **Train a neural network closure end-to-end** through the containerized solver, differentiating through the entire time-stepping loop.\n", "4. **Compare the learned closure against baselines** (a pure physics model and a pure ML model).\n", "\n", "We will replace the viscosity model of a Burgers' equation solver -- normally a hand-tuned constant -- with a small neural network, and train it so that it recovers the true (unknown) viscosity profile from solution data alone.\n", "\n", "## Context\n", "\n", "Closure modeling is a recurring problem across computational science: a simulation resolves the large scales but needs a model for the unresolved physics -- a turbulence closure, a sub-grid scheme, a constitutive law. These closures are often hand-tuned or empirical, and a natural idea is to *learn* them from data instead. The catch is that a closure only makes sense *inside* the solver: to train it well, gradients have to flow from the simulation output, through the solver, and back into the network.\n", "\n", "This is hard in practice because the solver is usually not a 30-line function you can `import` into your training script. It is a heavyweight simulator with its own runtime, dependencies, and adjoint -- a Fortran/C++ CFD code, a legacy in-house solver, or something differentiated by [Enzyme](https://enzyme.mit.edu/) at the LLVM IR level.\n", "\n", "With Tesseracts, we wrap the solver in a container that exposes a clean differentiable interface, and call it over HTTP as a single layer inside an otherwise ordinary PyTorch training loop. The neural network lives in native PyTorch; the solver lives in a Docker image. [Tesseract-Torch](https://github.com/pasteurlabs/tesseract-torch) registers the served solver as a PyTorch autograd custom function, so `loss.backward()` dispatches a vector-Jacobian product (VJP) call back through the solver automatically -- the analogue of what [Tesseract-JAX](https://github.com/pasteurlabs/tesseract-jax) does for JAX in the other demos in this series.\n", "\n", "### The components\n", "\n", "- **`burgers_solver`** (containerized Tesseract): a single-timestep Burgers' equation solver. Takes the current velocity field `u` and a viscosity field `nu`, returns the velocity field after one explicit Euler step -- a pure physics component with the interface $(u, \\nu, dt) \\to u_\\text{next}$. It exposes a VJP, so it slots into PyTorch autograd like any other layer, even though it runs in a separate container.\n", "- **`ViscosityNet`** (`torch.nn.Module`): a small MLP that maps local flow features $(u, \\partial u/\\partial x, x)$ to a viscosity field $\\nu$. An ordinary network, trained with a standard optimizer, in this process.\n", "\n", "The outer time-stepping loop calls the network directly and the solver via `apply_tesseract`:\n", "\n", "```python\n", "for each timestep:\n", " nu = viscosity_net(u, dudx, x) # plain torch, in-process\n", " u_next = apply_tesseract(solver, {u, nu, dt})[\"u_next\"] # HTTP call to container\n", " u = u_next\n", "```\n", "\n", "To learn more about building and running Tesseracts, please refer to the [Tesseract documentation](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FULL_RUN=False: 3 train / 2 test ICs, 40 steps, 100 epochs, dt=0.0003\n" ] } ], "source": [ "import os\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "from tesseract_torch import apply_tesseract\n", "\n", "from tesseract_core import Tesseract\n", "\n", "torch.set_default_dtype(torch.float64)\n", "\n", "# Workload size. Every solver call is an HTTP round-trip to the container, so\n", "# the defaults are modest to keep the demo (and CI) to a few minutes.\n", "# Set FULL_RUN=1 for the publication-quality run used to produce the figures.\n", "FULL_RUN = os.environ.get(\"FULL_RUN\", \"0\") == \"1\"\n", "\n", "# Time step. The spatially-varying viscosity only becomes identifiable once\n", "# diffusion has acted appreciably over the rollout, so we use a dt large enough\n", "# for the closure to actually \"feel\" the viscosity profile (well within the\n", "# explicit-diffusion CFL limit nu*dt/dx^2 < 0.5). Too small a dt and every\n", "# viscosity profile fits the data equally well — the closure can't recover the\n", "# shape no matter how long it trains.\n", "DT = 3e-4\n", "LR = 5e-3\n", "\n", "if FULL_RUN:\n", " N_TRAIN, N_TEST = 8, 4\n", " N_STEPS = 80\n", " N_EPOCHS = 300\n", " DIRECT_EPOCHS = 300\n", "else:\n", " # Small but enough to recover the viscosity hump and beat the constant\n", " # baseline.\n", " N_TRAIN, N_TEST = 3, 2\n", " N_STEPS = 40\n", " N_EPOCHS = 100\n", " DIRECT_EPOCHS = 100\n", "\n", "print(\n", " f\"FULL_RUN={FULL_RUN}: {N_TRAIN} train / {N_TEST} test ICs, \"\n", " f\"{N_STEPS} steps, {N_EPOCHS} epochs, dt={DT}\"\n", ")\n", "\n", "# Grid setup (must match the solver)\n", "N = 128\n", "DX = 1.0 / (N - 1)\n", "X = torch.linspace(0.0, 1.0, N)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Build and serve the solver Tesseract\n", "\n", "We build the solver into a Docker image with `tesseract build`, then serve it in a container and call it over HTTP -- the same way you would deploy a real simulator. The training loop below never imports the solver's code; it only ever talks to the running container.\n", "\n", "First, build the image (this can take a few minutes the first time):" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[2K \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;92ma256:1506\u001b[0mefb7dade, \u001b[1m[\u001b[0m\u001b[32m'burgers-solver:0.1.0'\u001b[0m, \u001b[32m'burgers-solver:latest'\u001b[0m\u001b[1m]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[\"burgers-solver:0.1.0\", \"burgers-solver:latest\"]\n" ] } ], "source": [ "%%bash\n", "tesseract build burgers_solver/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now load the built image and start a server container. `serve()` launches the container; we tear it down at the end of the notebook to free resources." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solver endpoints: ['apply', 'jacobian', 'jacobian_vector_product', 'vector_jacobian_product', 'health', 'abstract_eval', 'test']\n" ] } ], "source": [ "solver_tess = Tesseract.from_image(\"burgers-solver\")\n", "solver_tess.serve()\n", "print(f\"Solver endpoints: {solver_tess.available_endpoints}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: The ground truth -- Burgers' equation with spatially-varying viscosity\n", "\n", "We generate training data from a Burgers' equation with a **known but non-trivial** viscosity profile:\n", "\n", "$$\\nu_{\\text{true}}(x) = \\nu_0 \\left(1 + A \\sin(\\pi x)\\right)$$\n", "\n", "This represents a spatially-varying material property -- analogous to a turbulence closure, constitutive law, or sub-grid model that varies across the domain. The neural closure's job is to recover this profile from solution data alone.\n", "\n", "The reference solutions come from the **same served solver**, called forward-only (no gradients) -- there is no second copy of the physics anywhere in this notebook." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set: 3 initial conditions -> solutions\n", "Test set: 2 initial conditions -> solutions\n", "Grid: 128 points, dt=0.0003, 40 steps\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# True viscosity profile\n", "NU_0 = 0.02\n", "A = 0.8\n", "nu_true = NU_0 * (1.0 + A * torch.sin(np.pi * X))\n", "\n", "\n", "def burgers_reference(u0, nu_field, dt, n_steps):\n", " \"\"\"Reference solution with a prescribed viscosity field (no neural network).\n", "\n", " Calls the served solver Tesseract forward-only via `.apply()`. This is just\n", " data generation, so it stays outside autograd. `.apply()` returns decoded\n", " NumPy arrays, which we wrap back into tensors.\n", " \"\"\"\n", " u = u0.clone()\n", " for _ in range(n_steps):\n", " out = solver_tess.apply({\"u\": u, \"nu\": nu_field, \"dt\": dt})\n", " # .apply() returns a (possibly read-only) NumPy array decoded from JSON;\n", " # copy into a fresh tensor so PyTorch is happy.\n", " u = torch.tensor(np.asarray(out[\"u_next\"]))\n", " return u\n", "\n", "\n", "def make_ic(seed):\n", " \"\"\"Random smooth initial condition: sum of low-frequency sinusoids.\"\"\"\n", " rng = torch.Generator().manual_seed(seed)\n", " a1 = 0.5 + 0.5 * torch.rand(1, generator=rng).item()\n", " a2 = 0.3 * torch.rand(1, generator=rng).item()\n", " phase = torch.rand(1, generator=rng).item() * np.pi\n", " u0 = a1 * torch.sin(2 * np.pi * X + phase) + a2 * torch.sin(4 * np.pi * X)\n", " u0[0] = 0.0\n", " u0[-1] = 0.0\n", " return u0\n", "\n", "\n", "train_ics = torch.stack([make_ic(i) for i in range(N_TRAIN)])\n", "test_ics = torch.stack([make_ic(N_TRAIN + i) for i in range(N_TEST)])\n", "\n", "with torch.no_grad():\n", " train_targets = torch.stack(\n", " [burgers_reference(ic, nu_true, DT, N_STEPS) for ic in train_ics]\n", " )\n", " test_targets = torch.stack(\n", " [burgers_reference(ic, nu_true, DT, N_STEPS) for ic in test_ics]\n", " )\n", "\n", "print(f\"Training set: {N_TRAIN} initial conditions -> solutions\")\n", "print(f\"Test set: {N_TEST} initial conditions -> solutions\")\n", "print(f\"Grid: {N} points, dt={DT}, {N_STEPS} steps\")\n", "\n", "# Visualize one example\n", "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", "axes[0].plot(X.numpy(), train_ics[0].numpy(), label=\"Initial condition\")\n", "axes[0].plot(\n", " X.numpy(), train_targets[0].numpy(), label=f\"Solution at t={DT * N_STEPS:.3f}\"\n", ")\n", "axes[0].set_xlabel(\"x\")\n", "axes[0].set_ylabel(\"u\")\n", "axes[0].legend()\n", "axes[0].set_title(\"Example: Burgers' equation with true viscosity\")\n", "\n", "axes[1].plot(X.numpy(), nu_true.numpy(), \"k-\", linewidth=2)\n", "axes[1].set_xlabel(\"x\")\n", "axes[1].set_ylabel(r\"$\\nu(x)$\")\n", "axes[1].set_title(\"True viscosity profile (to be learned)\")\n", "axes[1].set_ylim(bottom=0)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: The neural closure -- a plain PyTorch module\n", "\n", "The closure is an ordinary `torch.nn.Module`: a small MLP with 2 hidden layers of 32 units. It maps the local flow features $(u, \\partial u/\\partial x, x)$ at each grid point to a viscosity value. There is nothing Tesseract-specific about it -- it is the network the closure researcher brings, trained with a standard optimizer, entirely in this process.\n", "\n", "A sigmoid keeps the predicted viscosity in a physically reasonable range $[0, \\nu_{\\max}]$, which also prevents CFL violations in the explicit solver." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Closure parameters: 1217\n", "Initial viscosity range: [0.0204, 0.0310]\n" ] } ], "source": [ "class ViscosityNet(nn.Module):\n", " \"\"\"MLP closure: local flow features (u, du/dx, x) -> viscosity nu.\"\"\"\n", "\n", " def __init__(self, hidden_dim=32, nu_max=0.05):\n", " super().__init__()\n", " self.nu_max = nu_max\n", " self.net = nn.Sequential(\n", " nn.Linear(3, hidden_dim),\n", " nn.Tanh(),\n", " nn.Linear(hidden_dim, hidden_dim),\n", " nn.Tanh(),\n", " nn.Linear(hidden_dim, 1),\n", " )\n", "\n", " def forward(self, u, dudx, x):\n", " features = torch.stack([u, dudx, x], dim=-1) # (N, 3)\n", " out = self.net(features)[:, 0] # (N,)\n", " return self.nu_max * torch.sigmoid(out)\n", "\n", "\n", "torch.manual_seed(1)\n", "closure = ViscosityNet()\n", "n_params = sum(p.numel() for p in closure.parameters())\n", "print(f\"Closure parameters: {n_params}\")\n", "\n", "# Sanity check: the untrained closure produces sensible viscosities\n", "with torch.no_grad():\n", " dudx0 = torch.gradient(train_ics[0], spacing=(DX,))[0]\n", " nu0 = closure(train_ics[0], dudx0, X)\n", "print(f\"Initial viscosity range: [{float(nu0.min()):.4f}, {float(nu0.max()):.4f}]\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: End-to-end training through the containerized solver\n", "\n", "The training loss is:\n", "\n", "$$\\mathcal{L}(\\theta) = \\frac{1}{M} \\sum_{i=1}^{M} \\left\\| u_{\\text{solver}}(u_0^{(i)}; \\nu_\\theta) - u_{\\text{data}}^{(i)} \\right\\|^2$$\n", "\n", "where $\\nu_\\theta$ is the neural viscosity closure with parameters $\\theta$, and $u_{\\text{solver}}$ runs the full Burgers' equation with $\\nu_\\theta$ called at every timestep.\n", "\n", "The time-stepping loop calls the network directly and the solver via `apply_tesseract`:\n", "\n", "```python\n", "for each timestep:\n", " nu = closure(u, dudx, x) # plain torch\n", " u = apply_tesseract(solver, {u, nu, dt})[\"u_next\"] # HTTP call to container\n", "```\n", "\n", "`loss.backward()` differentiates through the entire loop -- through every timestep, through a VJP HTTP request to the container at each step, and into the network weights -- automatically." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing forward pass + gradient...\n", " Initial loss: 1.356501e-04\n", " Gradient norm: 1.082265e-03\n", " Gradients flow: loss -> solver VJP (HTTP) -> network weights.\n" ] } ], "source": [ "def solve_with_closure(u0, closure, dt, n_steps):\n", " \"\"\"Run the full time-stepping loop, calling the served solver each step.\n", "\n", " The closure is plain in-process PyTorch; the solver is the containerized\n", " differentiable layer, reached over HTTP. In production this same loop would\n", " drive a Fortran solver with an adjoint — only the served image changes.\n", " \"\"\"\n", " u = u0\n", " for _step in range(n_steps):\n", " dudx = torch.zeros_like(u)\n", " dudx[1:-1] = (u[2:] - u[:-2]) / (2 * DX)\n", "\n", " nu = closure(u, dudx, X) # closure: predict viscosity (native torch)\n", "\n", " # Solver: one explicit Euler step, executed in the container\n", " solver_out = apply_tesseract(solver_tess, {\"u\": u, \"nu\": nu, \"dt\": dt})\n", " u = solver_out[\"u_next\"]\n", " return u\n", "\n", "\n", "def loss_batch(closure, ics, targets):\n", " \"\"\"Mean MSE over a batch of initial conditions.\"\"\"\n", " preds = torch.stack(\n", " [solve_with_closure(ics[i], closure, DT, N_STEPS) for i in range(ics.shape[0])]\n", " )\n", " return torch.mean((preds - targets) ** 2)\n", "\n", "\n", "# Verify the forward pass + gradient flow\n", "print(\"Testing forward pass + gradient...\")\n", "l0 = loss_batch(closure, train_ics, train_targets)\n", "l0.backward()\n", "grad_norm = torch.sqrt(\n", " sum(p.grad.pow(2).sum() for p in closure.parameters() if p.grad is not None)\n", ")\n", "print(f\" Initial loss: {float(l0.detach()):.6e}\")\n", "print(f\" Gradient norm: {float(grad_norm):.6e}\")\n", "print(\" Gradients flow: loss -> solver VJP (HTTP) -> network weights.\")\n", "closure.zero_grad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gradient validation against finite differences\n", "\n", "Correctness check: the AD gradient (loss → solver VJP over HTTP → network) matches finite differences to high precision." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AD FD Rel. Error\n", " -8.149260e-07 -8.149260e-07 1.34e-08\n" ] } ], "source": [ "# Finite difference check on a single weight element of the first layer.\n", "ics_sub = train_ics[:2]\n", "tgt_sub = train_targets[:2]\n", "\n", "# AD gradient\n", "closure.zero_grad()\n", "loss_val = loss_batch(closure, ics_sub, tgt_sub)\n", "loss_val.backward()\n", "\n", "w = closure.net[0].weight.data # first Linear layer (raw tensor, no autograd)\n", "idx = (0, 0)\n", "ad = float(closure.net[0].weight.grad[idx])\n", "\n", "# Finite difference\n", "eps = 1e-5\n", "orig = w[idx].item()\n", "with torch.no_grad():\n", " w[idx] = orig + eps\n", " l_plus = float(loss_batch(closure, ics_sub, tgt_sub))\n", " w[idx] = orig - eps\n", " l_minus = float(loss_batch(closure, ics_sub, tgt_sub))\n", " w[idx] = orig # restore\n", "\n", "fd = (l_plus - l_minus) / (2 * eps)\n", "rel_err = abs(ad - fd) / (abs(fd) + 1e-30)\n", "print(f\"{'AD':>14s} {'FD':>14s} {'Rel. Error':>12s}\")\n", "print(f\"{ad:14.6e} {fd:14.6e} {rel_err:12.2e}\")\n", "closure.zero_grad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training loop" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training neural closure through the solver...\n", " Epoch 0: train loss = 1.3565e-04, test loss = 9.9687e-06\n", " Epoch 10: train loss = 1.4967e-05, test loss = 8.3721e-06\n", " Epoch 20: train loss = 1.3913e-05, test loss = 7.6863e-06\n", " Epoch 30: train loss = 1.1806e-05, test loss = 5.5764e-06\n", " Epoch 40: train loss = 8.5923e-06, test loss = 5.1974e-06\n", " Epoch 50: train loss = 6.8999e-06, test loss = 4.4420e-06\n", " Epoch 60: train loss = 5.7348e-06, test loss = 3.9761e-06\n", " Epoch 70: train loss = 4.9379e-06, test loss = 3.6837e-06\n", " Epoch 80: train loss = 4.5155e-06, test loss = 3.8148e-06\n", " Epoch 90: train loss = 4.2492e-06, test loss = 3.8258e-06\n", " Epoch 99: train loss = 4.0613e-06, test loss = 3.7934e-06\n", "\n", "Final train loss: 4.0613e-06\n", "Final test loss: 3.7934e-06\n" ] } ], "source": [ "# Re-initialize for a clean training run\n", "torch.manual_seed(1)\n", "closure = ViscosityNet()\n", "optimizer = torch.optim.Adam(closure.parameters(), lr=LR)\n", "\n", "train_losses = []\n", "test_losses = []\n", "\n", "print(\"Training neural closure through the solver...\")\n", "for epoch in range(N_EPOCHS):\n", " optimizer.zero_grad()\n", " train_loss = loss_batch(closure, train_ics, train_targets)\n", " train_loss.backward()\n", " optimizer.step()\n", " train_losses.append(float(train_loss.detach()))\n", "\n", " if epoch % 10 == 0 or epoch == N_EPOCHS - 1:\n", " with torch.no_grad():\n", " test_loss = float(loss_batch(closure, test_ics, test_targets))\n", " test_losses.append((epoch, test_loss))\n", " print(\n", " f\" Epoch {epoch:4d}: train loss = {train_losses[-1]:.4e}, \"\n", " f\"test loss = {test_loss:.4e}\"\n", " )\n", "\n", "print(f\"\\nFinal train loss: {train_losses[-1]:.4e}\")\n", "print(f\"Final test loss: {test_losses[-1][1]:.4e}\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", "\n", "axes[0].semilogy(train_losses, label=\"Train\")\n", "test_epochs, test_vals = zip(*test_losses, strict=False)\n", "axes[0].semilogy(test_epochs, test_vals, \"o-\", label=\"Test\")\n", "axes[0].set_xlabel(\"Epoch\")\n", "axes[0].set_ylabel(\"MSE Loss\")\n", "axes[0].set_title(\"Training loss\")\n", "axes[0].legend()\n", "axes[0].grid(True, alpha=0.3)\n", "\n", "# Compare learned viscosity to true viscosity, averaged over training ICs.\n", "nu_samples = []\n", "with torch.no_grad():\n", " for ic in train_ics:\n", " dudx = torch.zeros_like(ic)\n", " dudx[1:-1] = (ic[2:] - ic[:-2]) / (2 * DX)\n", " nu_samples.append(closure(ic, dudx, X))\n", "nu_samples = torch.stack(nu_samples)\n", "nu_mean = nu_samples.mean(dim=0)\n", "nu_std = nu_samples.std(dim=0)\n", "\n", "x_np = X.numpy()\n", "axes[1].plot(x_np, nu_true.numpy(), \"k-\", linewidth=2, label=\"True viscosity\")\n", "axes[1].plot(x_np, nu_mean.numpy(), \"r--\", linewidth=2, label=\"Learned (mean over ICs)\")\n", "axes[1].fill_between(\n", " x_np,\n", " (nu_mean - nu_std).numpy(),\n", " (nu_mean + nu_std).numpy(),\n", " color=\"r\",\n", " alpha=0.15,\n", " label=\"Learned (std)\",\n", ")\n", "axes[1].set_xlabel(\"x\")\n", "axes[1].set_ylabel(r\"$\\nu$\")\n", "axes[1].set_title(\"Recovered viscosity profile\")\n", "axes[1].legend(fontsize=9)\n", "axes[1].set_ylim(bottom=0)\n", "axes[1].grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Baselines\n", "\n", "We compare three approaches:\n", "\n", "| Model | Description |\n", "|---|---|\n", "| **Constant viscosity** | Burgers' solver with $\\nu = \\nu_0$ (the standard \"wrong\" closure) |\n", "| **Direct ML** | MLP trained to map $u_0 \\to u_\\text{final}$ directly, no physics |\n", "| **Learned closure** | Neural $\\nu(u, \\partial u/\\partial x, x)$ trained through the solver (this demo) |" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Constant viscosity test MSE: 8.7019e-06\n", "\n", "Training direct ML baseline...\n", " Epoch 0: train = 3.3253e-01, test = 2.0101e-01\n", " Epoch 20: train = 3.6253e-02, test = 1.0957e-01\n", " Epoch 40: train = 4.0090e-03, test = 7.0837e-02\n", " Epoch 60: train = 1.2066e-03, test = 7.3164e-02\n", " Epoch 80: train = 6.3926e-04, test = 7.5776e-02\n", "\n", "Direct ML test MSE: 7.6441e-02\n", "Learned closure test MSE: 3.7934e-06\n", "\n", "Model Test MSE\n", "----------------------------------------\n", "Constant viscosity 8.7019e-06\n", "Direct ML 7.6441e-02\n", "Learned closure 3.7934e-06\n" ] } ], "source": [ "# --- Baseline 1: Constant (wrong) viscosity ---\n", "nu_const = NU_0 * torch.ones(N)\n", "with torch.no_grad():\n", " const_preds_test = torch.stack(\n", " [burgers_reference(ic, nu_const, DT, N_STEPS) for ic in test_ics]\n", " )\n", "const_mse = float(torch.mean((const_preds_test - test_targets) ** 2))\n", "print(f\"Constant viscosity test MSE: {const_mse:.4e}\")\n", "\n", "\n", "# --- Baseline 2: Direct ML (MLP mapping u0 -> u_final, no physics) ---\n", "class DirectNet(nn.Module):\n", " \"\"\"Pure ML: maps the whole field u0 directly to u_final.\"\"\"\n", "\n", " def __init__(self, n=N, hidden=128):\n", " super().__init__()\n", " self.net = nn.Sequential(\n", " nn.Linear(n, hidden),\n", " nn.Tanh(),\n", " nn.Linear(hidden, hidden),\n", " nn.Tanh(),\n", " nn.Linear(hidden, 64),\n", " nn.Tanh(),\n", " nn.Linear(64, n),\n", " )\n", "\n", " def forward(self, u0):\n", " return self.net(u0)\n", "\n", "\n", "def direct_loss(model, ics, targets):\n", " preds = model(ics)\n", " return torch.mean((preds - targets) ** 2)\n", "\n", "\n", "torch.manual_seed(2)\n", "direct = DirectNet()\n", "direct_opt = torch.optim.Adam(direct.parameters(), lr=1e-3)\n", "\n", "print(\"\\nTraining direct ML baseline...\")\n", "for epoch in range(DIRECT_EPOCHS):\n", " direct_opt.zero_grad()\n", " dl = direct_loss(direct, train_ics, train_targets)\n", " dl.backward()\n", " direct_opt.step()\n", " if epoch % 20 == 0:\n", " with torch.no_grad():\n", " test_dl = float(direct_loss(direct, test_ics, test_targets))\n", " print(\n", " f\" Epoch {epoch:4d}: train = {float(dl.detach()):.4e}, test = {test_dl:.4e}\"\n", " )\n", "\n", "with torch.no_grad():\n", " direct_test_mse = float(direct_loss(direct, test_ics, test_targets))\n", "print(f\"\\nDirect ML test MSE: {direct_test_mse:.4e}\")\n", "\n", "# --- Learned closure (already trained above) ---\n", "with torch.no_grad():\n", " learned_test_mse = float(loss_batch(closure, test_ics, test_targets))\n", "print(f\"Learned closure test MSE: {learned_test_mse:.4e}\")\n", "\n", "print(f\"\\n{'Model':<25s} {'Test MSE':>12s}\")\n", "print(\"-\" * 40)\n", "print(f\"{'Constant viscosity':<25s} {const_mse:12.4e}\")\n", "print(f\"{'Direct ML':<25s} {direct_test_mse:12.4e}\")\n", "print(f\"{'Learned closure':<25s} {learned_test_mse:12.4e}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 6: Solution comparison on test data\n", "\n", "For unseen initial conditions, we compare the three models' predictions against the ground truth." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_show = min(4, N_TEST)\n", "ncols = 2 if n_show > 1 else 1\n", "nrows = (n_show + ncols - 1) // ncols\n", "fig, axes = plt.subplots(nrows, ncols, figsize=(6 * ncols, 4.5 * nrows), squeeze=False)\n", "\n", "with torch.no_grad():\n", " direct_preds = direct(test_ics)\n", " for idx in range(n_show):\n", " ax = axes[idx // ncols][idx % ncols]\n", " ic = test_ics[idx]\n", " target = test_targets[idx]\n", "\n", " const_pred = burgers_reference(ic, nu_const, DT, N_STEPS)\n", " direct_pred = direct_preds[idx]\n", " learned_pred = solve_with_closure(ic, closure, DT, N_STEPS)\n", "\n", " ax.plot(x_np, target.numpy(), \"k-\", linewidth=2, label=\"Ground truth\")\n", " ax.plot(\n", " x_np,\n", " const_pred.numpy(),\n", " \"b--\",\n", " linewidth=1.5,\n", " alpha=0.7,\n", " label=\"Constant viscosity\",\n", " )\n", " ax.plot(\n", " x_np, direct_pred.numpy(), \"g:\", linewidth=1.5, alpha=0.7, label=\"Direct ML\"\n", " )\n", " ax.plot(\n", " x_np, learned_pred.numpy(), \"r-\", linewidth=1.5, label=\"Learned closure\"\n", " )\n", " ax.set_xlabel(\"x\")\n", " ax.set_ylabel(\"u\")\n", " ax.set_title(f\"Test case {idx + 1}\")\n", " ax.grid(True, alpha=0.3)\n", " if idx == 0:\n", " ax.legend(fontsize=9)\n", "\n", "plt.suptitle(\n", " \"Solution predictions on unseen initial conditions\", fontsize=13, fontweight=\"bold\"\n", ")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tear down the solver\n", "\n", "Stop the solver container to free resources." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Tear down Tesseract after use to prevent resource leaks\n", "solver_tess.teardown()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Takeaways\n", "\n", "In this tutorial, we trained a neural viscosity closure end-to-end through a containerized Burgers' equation solver. Here are the key points:\n", "\n", "1. **Containerized solver, native ML.** The Burgers' solver runs in a Docker container served as a Tesseract, while the neural viscosity closure is a plain in-process `torch.nn.Module`. The two are composed in an outer time-stepping loop: `closure(...)` then `apply_tesseract(solver, ...)` over HTTP at each step.\n", "\n", "2. **End-to-end gradients through the container.** Because Tesseract-Torch registers the served solver as a PyTorch autograd function, `loss.backward()` dispatches the solver's VJP over HTTP and flows the gradient into the network -- no hand-coded plumbing. We validated these gradients against finite differences.\n", "\n", "3. **Physics structure beats both extremes.** The learned closure achieves lower test MSE than either a constant-viscosity physics model or a pure ML model, recovering the true viscosity profile from solution data alone.\n", "\n", "4. **Composability across a container boundary.** The closure (plain torch) and the solver (a different image exposing the same $(u, \\nu, dt) \\to u_\\text{next}$ contract) can be swapped independently. The training loop is identical regardless of what language the solver is written in or how its adjoint is produced.\n", "\n", "### What's next\n", "\n", "- **Wrap a legacy solver.** Replace the Burgers' Tesseract with a Fortran/C++ solver that exposes an adjoint (for example via [Enzyme](https://enzyme.mit.edu/)). The closure training loop above works unchanged -- only the image name changes.\n", "- **Scale up.** Move to larger grids, 2D/3D problems, or richer closures (convolutional, attention-based). Batch multiple initial conditions per request to amortize HTTP overhead.\n", "- **Tackle a real closure.** Swap the Burgers' equation for a turbulence model, a climate sub-grid scheme, or a materials constitutive law.\n", "- **Explore other demos.** See the [CFD optimization](cfd-optimization.ipynb), [FEM shape optimization](fem-shape-optimization.ipynb), and [data assimilation](data-assimilation.ipynb) demos for the JAX side of the same composition pattern.\n", "\n", "Questions? Feedback? Please reach out through the [Tesseract Community Forum](https://si-tesseract.discourse.group/)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 4 }