{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# 2D stabilized Darcy Problem\n", "\n", "[![DownloadNotebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.1/resource/_static/logo_notebook_en.svg)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r2.1/mindflow/en/physics_driven/mindspore_darcy2D.ipynb) [![DownloadCode](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.1/resource/_static/logo_download_code_en.svg)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r2.1/mindflow/en/physics_driven/mindspore_darcy2D.py) [![ViewSource](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.1/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/r2.1/docs/mindflow/docs/source_en/physics_driven/darcy2D.ipynb)\n", "\n", "This notebook requires **MindSpore version >= 2.0.0** to support new APIs including: *mindspore.jit, mindspore.jit_class, mindspore.jacrev*.\n", "\n", "## Overview\n", "\n", "Darcy equation is a second-order, elliptic PDE (partial differential equation), which describes the flow through a porous medium at low speed. It is widely used in hydraulic engineering and petroleum engineering. The Darcy equation was originally formulated by Henry Darcy on the basis of experimental results of permeability experiments in sandy soil, and later derived from the Navier-Stokes equation by Stephen Whitaker via the homogenization method.\n", "\n", "It is difficult to obtain a generalized analytical solution of the Darcy equation for the permeability field of different fluids and the numerical method is usually used to solve the Darcy governing equation describing a specific scenario, and then the pressure field and velocity field of flow under the scenario are simulated. The numerical simulation results of Darcy flow can be used for further scientific research and engineering practice. Finite element method (FEM) for Darcy equation is designed to work with finite element spaces. In addition, for many problems of practical interest, some physical terms of the Darcy equation will be stabilized. The finite element method is designed in the standard finite element grid space. The higher accuracy required for numerical solution, the more fine the grid needs to be divided, and it costs larger time and storage overhead.\n", "\n", "As the research of parallel algorithm for numerical solution tends to slow down, the method based on neural network has been developed and achieved the solution accuracy close to the traditional numerical method. In 2019, the Applied Mathematics team of Brown University proposed a Physics-informed Neural Networks (PINNs) and provided a complete code framework to construct PINNs for solving a wide variety of PDEs. In this case, MindSpore Flow suite of fluid equations is used to solve the two-dimensional stabilized Darcy equation based on PINNs method.\n", "\n", "## Problem Description\n", "\n", "Considering the two-dimensional cube $\\Omega=(0, 1)\\times(0, 1)$, The boundary of the cube is $\\Gamma$. Ignoring the effects of gravity, in the range of $\\Omega$, the two-dimensional stabilized Darcy equation satisfied by the fluid pressure $p$ and velocity $u$ is as follows:\n", "\n", "$$\n", "\\begin{align}\n", "u + \\nabla p &= 0, (x, y)\\in\\Omega\\\\\n", "\\nabla \\cdot u &= f, (x, y)\\in\\Omega\n", "\\end{align}\n", "$$\n", "\n", "The Dirichlet boundary conditions are used in this case in the following form:\n", "\n", "$$\n", "\\begin{align}\n", "u_x &= -2 \\pi cos(2 \\pi x) cos(2 \\pi y) &(x, y)\\in\\Gamma\\\\\n", "u_y &= 2 \\pi sin(2 \\pi x) sin(2 \\pi y) &(x, y)\\in\\Gamma\\\\\n", "p &= sin(2 \\pi x) cos(2 \\pi y) &(x, y)\\in\\Gamma\n", "\\end{align}\n", "$$\n", "\n", "In which $f$ is **forcing function** in the Darcy equation. In this case, **forcing function** $f$ is used to learn the mapping $(x, y) \\mapsto (u, p)$ from position to corresponding physical quantities when **forcing function** $f$ is $8 \\pi^2 sin(2 \\pi x)cos(2 \\pi y)$. So that the solution of Darcy equation is realized.\n", "\n", "## Technology Path\n", "\n", "MindSpore Flow solves 2D constant Darcy problem is as follows:\n", "\n", "1. Dataset Construction.\n", "\n", "2. Model Construction.\n", "\n", "3. Optimizer.\n", "\n", "4. 2D Darcy.\n", "\n", "5. Model Training.\n", "\n", "6. Model Evaluation and Visualizetion." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import time\n", "\n", "import numpy as np\n", "import mindspore as ms\n", "\n", "from mindspore import nn, Tensor, ops, jit, set_seed, data_sink\n", "from mindspore import dtype as mstype\n", "from sympy import Function, symbols, sin, cos, pi" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The following `src` pacakage can be downloaded in [applications/physics_driven/darcy/src](https://gitee.com/mindspore/mindscience/tree/r0.3/MindFlow/applications/physics_driven/darcy/src)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from mindflow.utils import load_yaml_config\n", "from mindflow.cell import FCSequential\n", "from mindflow.pde import PDEWithLoss, sympy_to_mindspore\n", "\n", "\n", "from src import create_training_dataset, create_test_dataset\n", "from src import calculate_l2_error\n", "\n", "set_seed(123456)\n", "np.random.seed(123456)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# set context for training: using graph mode for high performance training with GPU acceleration\n", "config = load_yaml_config(\"darcy_cfg.yaml\")\n", "ms.set_context(mode=ms.GRAPH_MODE, device_target=\"GPU\", device_id=0)\n", "use_ascend = ms.get_context(attr_key='device_target') == \"Ascend\"" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset Construction\n", "\n", "For the training dataset, this case conducts random sampling according to the problem domain and boundary conditions. The sampling configuration information is as follows, and samples are collected according to uniform distribution. The problem domain of cube is constructed, and then the known problem domain and boundary are sampled.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# create train dataset\n", "geom_name = \"flow_region\"\n", "flow_train_dataset = create_training_dataset(config, geom_name)\n", "train_data = flow_train_dataset.create_dataset(\n", " batch_size=config[\"train_batch_size\"], shuffle=True, drop_remainder=True\n", ")\n", "\n", "# create test dataset\n", "test_input, test_label = create_test_dataset(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Construction\n", "\n", "This example uses a simple fully-connected network with a depth of 6 layers and the activation function is the tanh function." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# network model\n", "model = FCSequential(in_channels=config[\"model\"][\"input_size\"],\n", " out_channels=config[\"model\"][\"output_size\"],\n", " neurons=config[\"model\"][\"neurons\"],\n", " layers=config[\"model\"][\"layers\"],\n", " residual=config[\"model\"][\"residual\"],\n", " act=config[\"model\"][\"activation\"],\n", " weight_init=config[\"model\"][\"weight_init\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizer" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# optimizer\n", "params = model.trainable_params()\n", "optim = nn.Adam(params, learning_rate=config[\"optimizer\"][\"lr\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 2D Darcy\n", "\n", "The following Darcy2D defines the Darcy' problem. Specifically, it includes 2 parts: governing equation and boundary conditions." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class Darcy2D(PDEWithLoss):\n", " def __init__(self, model, loss_fn=nn.MSELoss()):\n", " self.x, self.y = symbols(\"x y\")\n", " self.u = Function(\"u\")(self.x, self.y)\n", " self.v = Function(\"v\")(self.x, self.y)\n", " self.p = Function(\"p\")(self.x, self.y)\n", " self.in_vars = [self.x, self.y]\n", " self.out_vars = [self.u, self.v, self.p]\n", " self.loss_fn = loss_fn\n", " self.bc_nodes = sympy_to_mindspore(self.bc(), self.in_vars, self.out_vars)\n", " super(Darcy2D, self).__init__(model, self.in_vars, self.out_vars)\n", "\n", " def force_function(self, x, y):\n", " return 8 * pi**2 * sin(2 * pi * x) * cos(2 * pi * y)\n", "\n", " def pde(self):\n", " loss_1 = (\n", " self.u.diff(self.x)\n", " + self.v.diff(self.y)\n", " - self.force_function(self.x, self.y)\n", " )\n", " loss_2 = self.u + self.p.diff(self.x)\n", " loss_3 = self.v + self.p.diff(self.y)\n", " return {\"loss_1\": loss_1, \"loss_2\": loss_2, \"loss_3\": loss_3}\n", "\n", " def bc(self):\n", " u_boundary = self.u - (-2 * pi * cos(2 * pi * self.x) * cos(2 * pi * self.y))\n", "\n", " v_boundary = self.v - (2 * pi * sin(2 * pi * self.x) * sin(2 * pi * self.y))\n", "\n", " p_boundary = self.p - (sin(2 * pi * self.x) * cos(2 * pi * self.y))\n", "\n", " return {\n", " \"u_boundary\": u_boundary,\n", " \"v_boundary\": v_boundary,\n", " \"p_boundary\": p_boundary,\n", " }\n", "\n", " def get_loss(self, pde_data, bc_data):\n", " pde_res = ops.Concat(1)(self.parse_node(self.pde_nodes, inputs=pde_data))\n", " pde_loss = self.loss_fn(\n", " pde_res, Tensor(np.array([0.0]).astype(np.float32), mstype.float32)\n", " )\n", "\n", " bc_res = ops.Concat(1)(self.parse_node(self.bc_nodes, inputs=bc_data))\n", " bc_loss = self.loss_fn(\n", " bc_res, Tensor(np.array([0.0]).astype(np.float32), mstype.float32)\n", " )\n", "\n", " return pde_loss + bc_loss" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Model Training\n", "\n", "With MindSpore version >= 2.0.0, we can use the functional programming for training neural networks." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def train():\n", " # define problem\n", " problem = Darcy2D(model)\n", "\n", " def forward_fn(pde_data, bc_data):\n", " return problem.get_loss(pde_data, bc_data)\n", "\n", " grad_fn = ms.value_and_grad(forward_fn, None, optim.parameters, has_aux=False)\n", "\n", " @jit\n", " def train_step(pde_data, bc_data):\n", " loss, grads = grad_fn(pde_data, bc_data)\n", " loss = ops.depend(loss, optim(grads))\n", " return loss\n", "\n", " epochs = config[\"train_epoch\"]\n", " steps_per_epochs = train_data.get_dataset_size()\n", " sink_process = data_sink(train_step, train_data, sink_size=1)\n", "\n", " for epoch in range(1, 1 + epochs):\n", " local_time_beg = time.time()\n", " model.set_train(True)\n", " for _ in range(steps_per_epochs):\n", " cur_loss = sink_process()\n", " print(f\"epoch: {epoch} train loss: {cur_loss} epoch time: {(time.time() - local_time_beg) * 1000 :.3f} ms\")\n", " model.set_train(False)\n", " if epoch % config[\"eval_interval_epochs\"] == 0:\n", " calculate_l2_error(model, test_input, test_label, config[\"train_batch_size\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u_boundary: u(x, y) + 2*pi*cos(2*pi*x)*cos(2*pi*y)\n", " Item numbers of current derivative formula nodes: 2\n", "v_boundary: v(x, y) - 2*pi*sin(2*pi*x)*sin(2*pi*y)\n", " Item numbers of current derivative formula nodes: 2\n", "p_boundary: p(x, y) - sin(2*pi*x)*cos(2*pi*y)\n", " Item numbers of current derivative formula nodes: 2\n", "loss_1: -8*pi**2*sin(2*pi*x)*cos(2*pi*y) + Derivative(u(x, y), x) + Derivative(v(x, y), y)\n", " Item numbers of current derivative formula nodes: 3\n", "loss_2: u(x, y) + Derivative(p(x, y), x)\n", " Item numbers of current derivative formula nodes: 2\n", "loss_3: v(x, y) + Derivative(p(x, y), y)\n", " Item numbers of current derivative formula nodes: 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "epoch: 100 train loss: 6.8784714 epoch time: 1523.571 ms\n", " predict total time: 534.3403816223145 ms\n", " l2_error: 0.5755849074109586\n", "==================================================================================================\n", "epoch: 200 train loss: 0.6278709 epoch time: 1471.620 ms\n", " predict total time: 145.03049850463867 ms\n", " l2_error: 0.045125807781619925\n", "==================================================================================================\n", "...\n", "epoch: 3800 train loss: 0.0044780443 epoch time: 1648.896 ms\n", " predict total time: 624.0160465240479 ms\n", " l2_error: 0.006336488966235181\n", "==================================================================================================\n", "epoch: 3900 train loss: 0.010450709 epoch time: 1453.108 ms\n", " predict total time: 3.2355785369873047 ms\n", " l2_error: 0.007389579493622406\n", "==================================================================================================\n", "epoch: 4000 train loss: 0.023211665 epoch time: 1587.883 ms\n", " predict total time: 293.90811920166016 ms\n", " l2_error: 0.008666194314787058\n", "==================================================================================================\n", "End-to-End total time: 6409.854037761688 s\n" ] } ], "source": [ "start_time = time.time()\n", "train()\n", "print(\"End-to-End total time: {} s\".format(time.time() - start_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Evaluation and Visualizetion\n", "\n", "After training, all data points in the flow field can be inferred. And related results can be visualized." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from src import visual\n", "visual(model, config)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.15" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1" } } }, "nbformat": 4, "nbformat_minor": 2 }