{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "788d3bb9",
   "metadata": {},
   "source": [
    "# Physics-Informed Neural Network (PINN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341afde2",
   "metadata": {},
   "source": [
    "[PINNs](https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125) are is a NNs trained on data and a loss that penalises violations of the governing differential equations and boundary/initial conditions, so its predictions obey the underlying physics.\\\n",
    "They can be useful in predicting behviour of physical systems, provided that obey known DEs, if we have a small amount of data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77b25f4",
   "metadata": {},
   "source": [
    "As an example, let's try to find the general solution of the 1D heat equation:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial u}{\\partial t} = \\alpha \\frac{\\partial^2 u}{\\partial x^2}\n",
    "$$\n",
    "\n",
    "where $u$ is the temperature, $t$ is time, $x$ is the 1D spatial coordinate and $\\alpha$ is a constant that determines how quickly the heat conducts through the material."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc843954",
   "metadata": {},
   "source": [
    "The initial condition (IC) is $u(x,0)=\\sin(\\pi x)$ and the boundary conditions (BCs) are $u(0,t)=u(1,t)=0$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a419a2c1",
   "metadata": {},
   "source": [
    "Let's train a NN and a PINN on only 10 data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a422f6a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "np.random.seed(290402)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f2d09e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.2 # Set alpha\n",
    "\n",
    "# Heat equation\n",
    "def temp(x,t):\n",
    "    return np.exp(-np.pi**2 * alpha * t) * np.sin(np.pi * x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01f6027d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 10 random points\n",
    "N = 10\n",
    "x = np.random.rand(N)\n",
    "t = np.random.rand(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1895a84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the data points for training\n",
    "xt = torch.tensor(np.stack([x, t], axis=1), dtype=torch.float32) \n",
    "u = torch.tensor(temp(x,t).reshape(-1,1), dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "215e7595",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a regular NN\n",
    "\n",
    "class RegularNN(nn.Module):\n",
    "    def __init__(self, in_dim=2, out_dim=1, hidden_dim=32):\n",
    "        super(RegularNN, self).__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(in_dim, hidden_dim),\n",
    "            nn.Tanh(),\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.Tanh(),\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.Tanh(),\n",
    "            nn.Linear(hidden_dim, out_dim)\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.net(x)\n",
    "    \n",
    "    def predict(self, x):\n",
    "        self.eval()\n",
    "        return self.net(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18286210",
   "metadata": {},
   "source": [
    "The PINN has the same architecture as the previous NN but we can use PyTorch's autodifferentiation to find $u_{xx}$ and $u_t$ and add regularisation terms to the loss:\n",
    "\n",
    "$$\n",
    "\\mathcal{L}_{\\mathrm{PDE}} = \\lambda_{\\mathrm{pde}}\\,\\frac{1}{N}\\sum_{i=1}^{N} \\left( u_t(x_i,t_i) - \\alpha\\,u_{xx}(x_i,t_i) \\right)^2\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\mathcal{L}_{\\mathrm{BC}} = \\lambda_{\\mathrm{BC}}\\,\\frac{1}{N}\\sum_{i=1}^{N} \\left( u(0,t_i)^2 + u(1,t_i)^2 \\right)\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\mathcal{L}_{\\mathrm{IC}} = \\lambda_{\\mathrm{IC}}\\,\\frac{1}{N}\\sum_{i=1}^{N} \\left( u(x_i,0) - \\sin(\\pi x_i) \\right)^2\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eea121f",
   "metadata": {},
   "source": [
    "Another advantage of using a PINN is that we can set constants of the PDE (in this case $\\alpha$) to be differentiable constants and we can extract their values from the trained PINN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f7c2441",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PINN(RegularNN):\n",
    "    def __init__(self, in_dim=2, out_dim=1, hidden_dim=32):\n",
    "        super().__init__(in_dim=in_dim, out_dim=out_dim, hidden_dim=hidden_dim)\n",
    "\n",
    "        self.type = 'pinn'\n",
    "        self.alpha = nn.Parameter(data=torch.tensor([0.])) # Differentiable constant\n",
    "\n",
    "    def pde_residual(self, xt):\n",
    "\n",
    "        xt = xt.requires_grad_(True)              \n",
    "        u  = self.forward(xt)                   \n",
    "\n",
    "        grads = torch.autograd.grad(\n",
    "            u, xt, torch.ones_like(u), create_graph=True\n",
    "        )[0]                                     \n",
    "        u_x = grads[:, 0:1]\n",
    "        u_t = grads[:, 1:2]\n",
    "\n",
    "        u_xx = torch.autograd.grad(\n",
    "            u_x, xt, torch.ones_like(u_x), create_graph=True\n",
    "        )[0][:, 0:1]\n",
    "\n",
    "        res = u_t - self.alpha * u_xx\n",
    "        return res\n",
    "    \n",
    "    def bc_residual(self, xt):\n",
    "            t  = xt[:, 1:2].detach()                    \n",
    "            x0 = torch.zeros_like(t)\n",
    "            x1 = torch.ones_like(t)\n",
    "\n",
    "            u0 = self.forward(torch.cat([x0, t], dim=1))  \n",
    "            u1 = self.forward(torch.cat([x1, t], dim=1))  \n",
    "\n",
    "            return torch.cat([u0, u1], dim=0)           \n",
    "\n",
    "    def ic_residual(self, xt):\n",
    "\n",
    "        x  = xt[:, 0:1].detach()                   \n",
    "        t0 = torch.zeros_like(x)\n",
    "\n",
    "        u_init = self.forward(torch.cat([x, t0], dim=1))\n",
    "        target = torch.sin(torch.pi * x)\n",
    "\n",
    "        return u_init - target                   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae50e4f7",
   "metadata": {},
   "source": [
    "Let's train the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9dc800b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "def train(model, xt, u, epochs=3000, lr=1e-3, weight_decay=0.0, device=\"mps\", verbose=False):\n",
    "    reg_pde = 1\n",
    "    reg_ic = 5\n",
    "    reg_bc = 5\n",
    "\n",
    "    model = model.to(device)\n",
    "    xt = xt.to(device)\n",
    "    u = u.to(device)\n",
    "\n",
    "    model.train()\n",
    "    opt = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    loss_fn = nn.MSELoss()\n",
    "\n",
    "    loss_hist = []\n",
    "    for ep in range(1, epochs+1):\n",
    "        opt.zero_grad()\n",
    "        pred = model(xt)\n",
    "        loss = loss_fn(pred, u)\n",
    "\n",
    "        if model.type == 'pinn':\n",
    "            pde_res = model.pde_residual(xt)\n",
    "            bc_res = model.bc_residual(xt)\n",
    "            ic_res = model.ic_residual(xt)\n",
    "            loss += reg_pde * (pde_res**2).mean() + reg_ic * (ic_res**2).mean() + reg_bc * (bc_res**2).mean()\n",
    "\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "        loss_hist.append(loss.item())\n",
    "        if verbose and ep % 500 == 0:\n",
    "            print(f\"[sup10] ep={ep} loss={loss.item():.3e}\")\n",
    "\n",
    "    return model, loss_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e78ca1da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train the regular NN\n",
    "regular_NN = RegularNN()\n",
    "regular_NN, _ = train(regular_NN, xt, u)\n",
    "\n",
    "# Save the mode weights|\n",
    "torch.save(regular_NN.state_dict(), 'regular_NN.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "127422db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the PINN\n",
    "pinn = PINN()\n",
    "pinn, _ = train(pinn, xt, u)\n",
    "\n",
    "# Save the mode weights\n",
    "torch.save(pinn.state_dict(), 'pinn.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ce6a4c",
   "metadata": {},
   "source": [
    "Let's plot the predicted data from the regular NN and the PINN and compare them with the true form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c227c590",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 18,\n",
    "    \"axes.titlesize\": 18,\n",
    "    \"axes.labelsize\": 18,\n",
    "    \"xtick.labelsize\": 18,\n",
    "    \"ytick.labelsize\": 18,\n",
    "    \"legend.fontsize\": 18,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72ae365e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def make_2d_hists(nn_model, pinn_model, alpha=1.0, Nx=100, Nt=100):\n",
    "    # space–time grid\n",
    "    x = np.linspace(0.0, 1.0, Nx)\n",
    "    t = np.linspace(0.0, 1.0, Nt)\n",
    "    X, T = np.meshgrid(x, t, indexing=\"ij\")  # X: (Nx,Nt), T: (Nx,Nt)\n",
    "\n",
    "    # True solution\n",
    "    U_exact = np.exp(-np.pi**2 * alpha * T) * np.sin(np.pi * X)\n",
    "\n",
    "    def predict_grid(model):\n",
    "        model.eval()\n",
    "        device = next(model.parameters()).device\n",
    "        XT = np.stack([X.ravel(), T.ravel()], axis=1)  # (Nx*Nt, 2) with [x,t]\n",
    "        xt = torch.tensor(XT, dtype=torch.float32, device=device)\n",
    "        with torch.no_grad():\n",
    "            U = model(xt).reshape(Nx, Nt).detach().cpu().numpy()\n",
    "        return U\n",
    "\n",
    "    U_nn   = predict_grid(nn_model)\n",
    "    U_pinn = predict_grid(pinn_model)\n",
    "\n",
    "    vmin = min(U_exact.min(), U_nn.min(), U_pinn.min())\n",
    "    vmax = max(U_exact.max(), U_nn.max(), U_pinn.max())\n",
    "\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(12, 4), constrained_layout = True)\n",
    "    data_plots = [(\"True\", U_exact), (\"Regular NN\", U_nn), (\"PINN\", U_pinn)]\n",
    "\n",
    "    for ax, (title, U) in zip(axes, data_plots):\n",
    "        im = ax.imshow(\n",
    "            U,\n",
    "            origin=\"lower\",\n",
    "            extent=[0, 1, 0, 1],\n",
    "            aspect=\"auto\",\n",
    "            cmap=\"Blues\",\n",
    "            vmin=vmin,\n",
    "            vmax=vmax\n",
    "        )\n",
    "        ax.set_xlabel(\"t\")\n",
    "        ax.set_ylabel(\"x\")\n",
    "        ax.set_title(title)\n",
    "\n",
    "    # single shared colorbar\n",
    "    cbar = fig.colorbar(\n",
    "        im,\n",
    "        ax=axes,\n",
    "        location=\"right\",\n",
    "        fraction=0.025,\n",
    "        pad=0.02,\n",
    "    )\n",
    "    cbar.set_label(r\"$u(x,t)$\")\n",
    "\n",
    "    plt.savefig(\"pinn.png\", dpi = 300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "599098e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_2d_hists(regular_NN, pinn, alpha=alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd056f55",
   "metadata": {},
   "source": [
    "The PINN was much better at predicting the true temperatures than the regular NN with the same architecture."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed375e45",
   "metadata": {},
   "source": [
    "Let's see what the PINN predicts $\\alpha$ to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89a45541",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted alpha = 0.196\n"
     ]
    }
   ],
   "source": [
    "print(f\"predicted alpha = {float(pinn.alpha):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "befec8e2",
   "metadata": {},
   "source": [
    "This is a pretty good estimate from the true $\\alpha=0.2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18771b15",
   "metadata": {},
   "source": [
    "Now let's see if we are able to distill the solution to the 1-D heat equation from the PINN."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7996f1",
   "metadata": {},
   "source": [
    "## Use PyTorch to approximate the behaviour of the PINN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6475c815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected IPython. Loading juliacall extension. See https://juliapy.github.io/PythonCall.jl/stable/compat/#IPython\n"
     ]
    }
   ],
   "source": [
    "from symtorch import SymbolicModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b00f1ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_data = 5000 # Create 5000 different data points\n",
    "sample_data = torch.tensor(np.random.rand(num_data, 2), dtype=torch.float32) \n",
    "pinn.net = SymbolicModel(pinn.net, 'pinn')\n",
    "\n",
    "sr_params = {'niterations': 1000,\n",
    "             'constraints': {'sin':3, 'exp':3}, \n",
    "             'complexity_of_operators': {'sin':3, 'exp':3},\n",
    "             \"unary_operators\": [\"inv(x) = 1/x\", \"sin\", \"exp\"],\n",
    "             'parsimony': 0.01,\n",
    "             'nested_constraints':{'sin':{'sin':0, 'exp':0}, 'exp':{'exp':0, 'sin':0}},\n",
    "             'verbosity': 0\n",
    "             }\n",
    "\n",
    "variable_names = ['x', 't']\n",
    "fit_params = {'variable_names': variable_names}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "166da776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🛠️ Running SR on output dimension 0 of 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liz/PhD/SymTorch_project/symtorch_venv/lib/python3.11/site-packages/pysr/sr.py:2811: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💡Best equation for output 0 found to be (sin(x * -3.1416416) * ((exp(t * -2.108336) * -0.9574661) + -0.044413462)) + ((t * (t * t)) * -0.026951205).\n",
      "❤️ SR on pinn complete.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0: PySRRegressor.equations_ = [\n",
       " \t    pick     score                                           equation  \\\n",
       " \t0         0.000000                                          0.2781431   \n",
       " \t1         0.050918                                 inv(t + 2.8472147)   \n",
       " \t2         0.511774                      (t * -0.5400548) + 0.54739565   \n",
       " \t3         0.065848                  inv(t + 0.90205467) + -0.46818647   \n",
       " \t4         0.290576                     x * (inv(t + x) + -0.47152975)   \n",
       " \t5         0.753261             (t + -0.9360194) * sin(x * -3.1696231)   \n",
       " \t6         0.459206  (x * ((t + -1.0282526) * (x + -0.9901195))) * ...   \n",
       " \t7         0.265628  x * ((x + -0.9911626) * (inv(t + 0.23931801) *...   \n",
       " \t8         3.847941            sin(x * 3.1444418) * exp(t * -1.980765)   \n",
       " \t9         0.002700  sin(x * -3.1448305) * (exp(t * -1.9846908) * -...   \n",
       " \t10        0.175277  (sin(x * -3.1372554) * (exp(t * -1.941653) * -...   \n",
       " \t11        0.150502  (sin(x * -3.1405466) * (exp(t * -1.9165117) * ...   \n",
       " \t12        0.049582  ((sin(x * -3.1424396) * exp(t * -1.9286698)) *...   \n",
       " \t13        0.163645  ((t * t) * -0.02136984) + (sin(x * -3.1408503)...   \n",
       " \t14  >>>>  0.204466  (sin(x * -3.1416416) * ((exp(t * -2.108336) * ...   \n",
       " \t15        0.095385  ((t * t) * ((t * t) * -0.028219905)) + (((exp(...   \n",
       " \t16        0.001183  (((t * t) * ((t * t) * -0.027965615)) + (sin(x...   \n",
       " \t\n",
       " \t        loss  complexity  \n",
       " \t0   0.049377           1  \n",
       " \t1   0.042382           4  \n",
       " \t2   0.025405           5  \n",
       " \t3   0.023786           6  \n",
       " \t4   0.013303           8  \n",
       " \t5   0.002949          10  \n",
       " \t6   0.001863          11  \n",
       " \t7   0.001428          12  \n",
       " \t8   0.000030          13  \n",
       " \t9   0.000030          15  \n",
       " \t10  0.000021          17  \n",
       " \t11  0.000016          19  \n",
       " \t12  0.000014          21  \n",
       " \t13  0.000010          23  \n",
       " \t14  0.000007          25  \n",
       " \t15  0.000006          27  \n",
       " \t16  0.000006          29  \n",
       " ]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinn.net.distill(sample_data.to(torch.device('mps')), sr_params = sr_params,\n",
    "                 fit_params=fit_params\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8271ef83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "➡️ Dimension 0 - Complexity 13:\n",
      "   sin(x * 3.1444418) * exp(t * -1.980765) (loss: 3.046010e-05)\n"
     ]
    }
   ],
   "source": [
    "pinn.net.show_symbolic_expression(complexity=[13])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "848d5cde",
   "metadata": {},
   "source": [
    "The correct equation is of the form\n",
    "\n",
    "$$\n",
    "u(x,t) = e^{-\\pi^2 \\alpha t} \\sin(\\pi x) \\approx e^{-1.97} \\sin (3.14 x)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1331e222",
   "metadata": {},
   "source": [
    "PySR's best equation returns correct form of the equation! \\\n",
    "(For more reliable SR performance, you should increase `niterations` until the Pareto front stablises, which may be >1000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "37682d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Successfully switched pinn to symbolic equations for all 1 dimensions:\n",
      "   Dimension 0: sin(x * 3.1444418) * exp(t * -1.980765)\n",
      "   Variables: ['t', 'x']\n",
      "🎯 All 1 output dimensions now using symbolic equations.\n"
     ]
    }
   ],
   "source": [
    "pinn.net.switch_to_symbolic(complexity=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f92d209f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "import os\n",
    "if os.path.exists('SR_output'):\n",
    "    shutil.rmtree('SR_output')\n",
    "    os.remove('pinn.pth')\n",
    "    os.remove('regular_NN.pth')"
   ]
  }
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