diff --git a/docs/examples-pinn-forward/Laplace_disk.ipynb b/docs/examples-pinn-forward/Laplace_disk.ipynb index 8422b61..fc5ebf6 100644 --- a/docs/examples-pinn-forward/Laplace_disk.ipynb +++ b/docs/examples-pinn-forward/Laplace_disk.ipynb @@ -26,11 +26,374 @@ "\n", "The reference solution is $y=r\\cos(\\theta)$." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "This description goes through the implementation of a solver for the above described Heat equation step-by-step.\n", + "\n", + "First, import the libraries we need:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import brainstate as bst\n", + "import brainunit as u\n", + "import numpy as np\n", + "\n", + "import pinnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by defining a computational geometry. We can use a built-in class `Rectangle` as follows" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "geom = pinnx.geometry.Rectangle(\n", + " xmin=[0, 0],\n", + " xmax=[1, 2 * np.pi],\n", + ").to_dict_point(r=u.meter, theta=u.radian)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we express the PDE residual of the Laplace equation:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def pde(x, y):\n", + " jacobian = net.jacobian(x)\n", + " hessian = net.hessian(x)\n", + "\n", + " dy_r = jacobian[\"y\"][\"r\"]\n", + " dy_rr = hessian[\"y\"][\"r\"][\"r\"]\n", + " dy_thetatheta = hessian[\"y\"][\"theta\"][\"theta\"]\n", + " return x['r'] * dy_r + x['r'] ** 2 * dy_rr + dy_thetatheta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first argument to `pde` is 2-dimensional vector where the first component(`x[:,0:1]`) is $r$-coordinate and the second componenet (`x[:,1:]`) is the $\\theta$-coordinate. The second argument is the network output, i.e., the solution $y(r, \\theta)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we consider the Dirichlet boundary condition. We need to implement a function, which should return `True` for points inside the subdomain and `False` for the points outside. In our case, if the points satisfy $r=1$ and are on the whole boundary of the rectangle domain, then function `boundary` returns `True`. Otherwise, it returns `False`. (Note that because of rounding-off errors, it is often wise to use u.math.allclose to test whether two floating point values are equivalent.)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def boundary(x, on_boundary):\n", + " return on_boundary and u.math.allclose(x['r'], 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The argument `x` to `boundary` is the network input and is a $d$-dim vector, where $d$ is the dimension and $d=2$ in this case. To facilitate the implementation of `boundary`, a boolean `on_boundary` is used as the second argument. If the point $r,\\theta$ (the first argument) is on the entire boundary of the rectangle geometry that created above, then `on_boundary` is `True`, otherwise, `on_boundary` is False." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using a lambda funtion, the `boundary` we defined above can be passed to `DirichletBC` as the second argument. Thus, the Dirichlet boundary condition is" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "uy = u.volt / u.meter\n", + "bc = pinnx.icbc.DirichletBC(\n", + " lambda x: {'y': u.math.cos(x['theta']) * uy},\n", + " lambda x, on_boundary: u.math.logical_and(on_boundary, u.math.allclose(x['r'], 1 * u.meter)),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we rewrite this problem in cartesian coordinates, the variables are in the form of $[r\\sin(\\theta), r\\cos(\\theta)]$. We use them as features to satisfy the certain underlying physical constraints, so that the network is automatically periodic along the $\\theta$ coordinate and the period is $2\\pi$.\n", + "\n", + "Next, we choose the network. Here, we use a fully connected neural network of depth 4 (i.e., 3 hidden layers) and width 20:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Use [r*sin(theta), r*cos(theta)] as features,\n", + "# so that the network is automatically periodic along the theta coordinate.\n", + "def feature_transform(x):\n", + " x = pinnx.utils.array_to_dict(x, [\"r\", \"theta\"], keep_dim=True)\n", + " return u.math.concatenate([x['r'] * u.math.sin(x['theta']),\n", + " x['r'] * u.math.cos(x['theta'])], axis=-1)\n", + "\n", + "net = pinnx.nn.Model(\n", + " pinnx.nn.DictToArray(r=u.meter, theta=u.radian),\n", + " pinnx.nn.FNN([2] + [20] * 3 + [1], \"tanh\", input_transform=feature_transform),\n", + " pinnx.nn.ArrayToDict(y=uy),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we have specified the geometry, PDE residual, and boundary condition. We then define the `PDE` problem as" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The argument `solution` is the reference solution to compute the error of our solution, and we define it as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def solution(x):\n", + " r, theta = x['r'], x['theta']\n", + " return {'y': r * u.math.cos(theta) * uy / u.meter}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "problem = pinnx.problem.PDE(\n", + " geom,\n", + " pde,\n", + " bc,\n", + " net,\n", + " num_domain=2540,\n", + " num_boundary=80,\n", + " solution=solution\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we have the PDE problem and the network. We bulid a `trainer` and choose the optimizer and learning rate:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling trainer...\n", + "'compile' took 0.093740 s\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer = pinnx.Trainer(problem)\n", + "trainer.compile(bst.optim.Adam(1e-3), metrics=[\"l2 relative error\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then train the model for 15000 iterations:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training trainer...\n", + "\n", + "Step Train loss Test loss Test metric \n", + "0 [3.4136772 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [3.4136772 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(1.9016247, dtype=float32)}] \n", + " {'ibc0': {'y': 1.2442183 * volt / meter}}] {'ibc0': {'y': 1.2442183 * volt / meter}}] \n", + "1000 [0.00209501 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [0.00209501 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.03482116, dtype=float32)}] \n", + " {'ibc0': {'y': 9.6204014e-05 * volt / meter}}] {'ibc0': {'y': 9.6204014e-05 * volt / meter}}] \n", + "2000 [0.00059394 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [0.00059394 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.02848322, dtype=float32)}] \n", + " {'ibc0': {'y': 1.8821578e-05 * volt / meter}}] {'ibc0': {'y': 1.8821578e-05 * volt / meter}}] \n", + "3000 [0.0003004 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [0.0003004 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.01964555, dtype=float32)}] \n", + " {'ibc0': {'y': 1.1315266e-05 * volt / meter}}] {'ibc0': {'y': 1.1315266e-05 * volt / meter}}] \n", + "4000 [0.0001739 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [0.0001739 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.0129396, dtype=float32)}] \n", + " {'ibc0': {'y': 8.526638e-06 * volt / meter}}] {'ibc0': {'y': 8.526638e-06 * volt / meter}}] \n", + "5000 [0.00010057 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [0.00010057 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00673189, dtype=float32)}] \n", + " {'ibc0': {'y': 6.3102784e-06 * volt / meter}}] {'ibc0': {'y': 6.3102784e-06 * volt / meter}}] \n", + "6000 [5.6971734e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [5.6971734e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00247048, dtype=float32)}] \n", + " {'ibc0': {'y': 4.33217e-06 * volt / meter}}] {'ibc0': {'y': 4.33217e-06 * volt / meter}}] \n", + "7000 [3.255158e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [3.255158e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.0019735, dtype=float32)}] \n", + " {'ibc0': {'y': 2.83005e-06 * volt / meter}}] {'ibc0': {'y': 2.83005e-06 * volt / meter}}] \n", + "8000 [2.4938374e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [2.4938374e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00377944, dtype=float32)}] \n", + " {'ibc0': {'y': 4.333744e-06 * volt / meter}}] {'ibc0': {'y': 4.333744e-06 * volt / meter}}] \n", + "9000 [1.1950426e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [1.1950426e-05 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00174527, dtype=float32)}] \n", + " {'ibc0': {'y': 1.2896384e-06 * volt / meter}}] {'ibc0': {'y': 1.2896384e-06 * volt / meter}}] \n", + "10000 [8.125793e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [8.125793e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00141424, dtype=float32)}] \n", + " {'ibc0': {'y': 9.607884e-07 * volt / meter}}] {'ibc0': {'y': 9.607884e-07 * volt / meter}}] \n", + "11000 [7.288996e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [7.288996e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00191965, dtype=float32)}] \n", + " {'ibc0': {'y': 1.3772864e-06 * volt / meter}}] {'ibc0': {'y': 1.3772864e-06 * volt / meter}}] \n", + "12000 [5.566375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [5.566375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00146894, dtype=float32)}] \n", + " {'ibc0': {'y': 9.980397e-07 * volt / meter}}] {'ibc0': {'y': 9.980397e-07 * volt / meter}}] \n", + "13000 [4.0166346e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [4.0166346e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00078307, dtype=float32)}] \n", + " {'ibc0': {'y': 4.9924233e-07 * volt / meter}}] {'ibc0': {'y': 4.9924233e-07 * volt / meter}}] \n", + "14000 [3.4733355e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [3.4733355e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.00065782, dtype=float32)}] \n", + " {'ibc0': {'y': 4.2479667e-07 * volt / meter}}] {'ibc0': {'y': 4.2479667e-07 * volt / meter}}] \n", + "15000 [4.31375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [4.31375e-06 * 10.0^0 * (meter * (volt / meter) / meter) ** 2, [{'y': Array(0.0016097, dtype=float32)}] \n", + " {'ibc0': {'y': 1.0574405e-06 * volt / meter}}] {'ibc0': {'y': 1.0574405e-06 * volt / meter}}] \n", + "\n", + "Best trainer at step 14000:\n", + " train loss: 3.90e-06\n", + " test loss: 3.90e-06\n", + " test metric: [{'y': Array(0., dtype=float32)}]\n", + "\n", + "'train' took 56.701270 s\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.train(iterations=15000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also save and plot the best trained result and loss history." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving loss history to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat ...\n", + "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/loss.dat\n", + "Saving training data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat ...\n", + "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/train.dat\n", + "Saving test data to /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat ...\n", + "Saving checkpoint into /Users/sichaohe/Documents/GitHub/pinnx/docs/examples-pinn-forward/test.dat\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainer.saveplot(issave=True, isplot=True)" + ] } ], "metadata": { + "kernelspec": { + "display_name": "pinnx", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" } }, "nbformat": 4, diff --git a/docs/examples-pinn-forward/heat_resample.py b/docs/examples-pinn-forward/heat_resample.py index 35ff14e..175f34a 100644 --- a/docs/examples-pinn-forward/heat_resample.py +++ b/docs/examples-pinn-forward/heat_resample.py @@ -98,7 +98,6 @@ def pde(x, y): dy_xx = hessian['y']['x']['x'] return dy_t - a * dy_xx -pde_resampler = pinnx.callbacks.PDEPointResampler(period=10) # Define the PDE problem and configurations of the network: @@ -127,6 +126,8 @@ def pde(x, y): # Build and train the trainer: trainer.compile(bst.optim.Adam(1e-3)) + +pde_resampler = pinnx.callbacks.PDEPointResampler(period=10) trainer.train(iterations=10000, callbacks=[pde_resampler]) trainer.compile(bst.optim.OptaxOptimizer(optax.lbfgs(1e-3, linesearch=None))) # TODO: train method must has iteration param