{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# 作用于圆环的二维Poisson问题\n", "\n", "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_notebook.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r2.0.0-alpha/mindflow/zh_cn/physics_driven/mindspore_poisson_ring.ipynb) [![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_download_code.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r2.0.0-alpha/mindflow/zh_cn/physics_driven/mindspore_poisson_ring.py) [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r2.0.0-alpha/docs/mindflow/docs/source_zh_cn/physics_driven/poisson_ring.ipynb)\n", "\n", "本案例要求**MindSpore版本 >= 2.0.0**调用如下接口: *mindspore.jit,mindspore.jit_class,mindspore.jacrev*。\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 概述\n", "\n", "泊松方程是一个在理论物理中具有广泛效用的椭圆偏微分方程。例如,泊松方程的解是由给定电荷或质量密度分布引起的势场;在已知势场的情况下,可以计算静电或引力(力)场。" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 问题描述\n", "\n", "我们从二维齐次泊松方程出发,\n", "\n", "$$\n", "f + \\Delta u = 0\n", "$$\n", "\n", "其中 `u` 是主变量, `f` 是源项, $\\Delta$ 表示拉普拉斯运算符。\n", "\n", "我们考虑源项 `f` ($f=1.0$)则泊松方程可以表示为:\n", "\n", "$$\n", "\\frac{\\partial^2u}{\\partial x^2} + \\frac{\\partial^2u}{\\partial y^2} + 1.0 = 0,\n", "$$\n", "\n", "本案例中,使用Dirichlet边界条件和Neumann边界条件。格式如下:\n", "\n", "外圆边界上的Dirichlet边界条件:\n", "\n", "$$\n", "u = 0\n", "$$\n", "\n", "内圆边界上的Neumann边界条件:\n", "\n", "$$\n", "du/dn = 0\n", "$$\n", "\n", "本案例利用PINNs方法学习 $(x, y) \\mapsto u$,实现泊松方程的求解。" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 技术路径\n", "\n", "MindFlow求解该问题的具体流程如下:\n", "\n", "1. 创建数据集。\n", "2. 构建模型。\n", "3. 优化器。\n", "4. Poisson2D。\n", "5. 模型训练。\n", "6. 模型推理及可视化。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "import sympy\n", "\n", "from mindspore import nn, ops, Tensor, set_context, set_seed, jit\n", "from mindspore import dtype as mstype\n", "import mindspore as ms\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "下述`src`包可以在[applications/physics_driven/poisson_ring/src](https://gitee.com/mindspore/mindscience/tree/r0.2.0-alpha/MindFlow/applications/physics_driven/poisson_ring/src)下载。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from mindflow.pde import Poisson, sympy_to_mindspore\n", "from mindflow.cell import MultiScaleFCCell\n", "from mindflow.utils import load_yaml_config\n", "\n", "from src import create_training_dataset, create_test_dataset, calculate_l2_error, visual_result\n", "\n", "\n", "set_seed(123456)\n", "set_context(mode=ms.GRAPH_MODE, device_target=\"GPU\", device_id=5)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "加载 `poisson2d_cfg.yaml` , 用户可以修改[配置文件](https://gitee.com/mindspore/mindscience/blob/r0.2.0-alpha/MindFlow/applications/physics_driven/poisson_ring/poisson2d_cfg.yaml)中的参数。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# load configurations\n", "config = load_yaml_config('poisson2d_cfg.yaml')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 创建数据集\n", "\n", "本案例根据求解域、边值条件进行随机采样,生成训练数据集与测试数据集,具体设置如下:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# create training dataset\n", "dataset = create_training_dataset(config)\n", "train_dataset = dataset.batch(batch_size=config[\"train_batch_size\"])\n", "\n", "# create test dataset\n", "inputs, label = create_test_dataset(config)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 构建模型\n", "\n", "本例使用简单的全连接网络,深度为6层,激活函数为`tanh`函数。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# define models and optimizers\n", "model = MultiScaleFCCell(in_channels=config[\"model\"][\"in_channels\"],\n", " out_channels=config[\"model\"][\"out_channels\"],\n", " layers=config[\"model\"][\"layers\"],\n", " neurons=config[\"model\"][\"neurons\"],\n", " residual=config[\"model\"][\"residual\"],\n", " act=config[\"model\"][\"activation\"],\n", " num_scales=1)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 优化器" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "optimizer = nn.Adam(model.trainable_params(), config[\"optimizer\"][\"initial_lr\"])\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Poisson2D\n", "\n", "`Poisson2D`包含求解问题的控制方程、狄利克雷边界条件、诺曼边界条件等。使用`sympy`以符号形式定义偏微分方程并求解所有方程的损失值。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class Poisson2D(Poisson):\n", " def __init__(self, model, loss_fn=nn.MSELoss()):\n", " super(Poisson2D, self).__init__(model, loss_fn=loss_fn)\n", " self.bc_outer_nodes = sympy_to_mindspore(self.bc_outer(), self.in_vars, self.out_vars)\n", " self.bc_inner_nodes = sympy_to_mindspore(self.bc_inner(), self.in_vars, self.out_vars)\n", "\n", " def bc_outer(self):\n", " bc_outer_eq = self.u\n", " equations = {\"bc_outer\": bc_outer_eq}\n", " return equations\n", "\n", " def bc_inner(self):\n", " bc_inner_eq = sympy.Derivative(self.u, self.normal) - 0.5\n", " equations = {\"bc_inner\": bc_inner_eq}\n", " return equations\n", "\n", " def get_loss(self, pde_data, bc_outer_data, bc_inner_data, bc_inner_normal):\n", " pde_res = self.parse_node(self.pde_nodes, inputs=pde_data)\n", " pde_loss = self.loss_fn(pde_res[0], Tensor(np.array([0.0]), mstype.float32))\n", "\n", " bc_inner_res = self.parse_node(self.bc_inner_nodes, inputs=bc_inner_data, norm=bc_inner_normal)\n", " bc_inner_loss = self.loss_fn(bc_inner_res[0], Tensor(np.array([0.0]), mstype.float32))\n", "\n", " bc_outer_res = self.parse_node(self.bc_outer_nodes, inputs=bc_outer_data)\n", " bc_outer_loss = self.loss_fn(bc_outer_res[0], Tensor(np.array([0.0]), mstype.float32))\n", "\n", " return pde_loss + bc_inner_loss + bc_outer_loss\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 模型训练\n", "\n", "使用**MindSpore >= 2.0.0**的版本,采用函数式编程的方式训练网络。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def train():\n", " problem = Poisson2D(model)\n", "\n", " def forward_fn(pde_data, bc_outer_data, bc_inner_data, bc_inner_normal):\n", " loss = problem.get_loss(pde_data, bc_outer_data, bc_inner_data, bc_inner_normal)\n", " return loss\n", "\n", " grad_fn = ops.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=False)\n", "\n", " @jit\n", " def train_step(pde_data, bc_outer_data, bc_inner_data, bc_inner_normal):\n", " loss, grads = grad_fn(pde_data, bc_outer_data, bc_inner_data, bc_inner_normal)\n", " loss = ops.depend(loss, optimizer(grads))\n", " return loss\n", "\n", " steps = config[\"train_steps\"]\n", " sink_process = ms.data_sink(train_step, train_dataset, sink_size=1)\n", " model.set_train()\n", " for step in range(steps):\n", " local_time_beg = time.time()\n", " cur_loss = sink_process()\n", " if step % 100 == 0:\n", " print(f\"loss: {cur_loss.asnumpy():>7f}\")\n", " print(\"step: {}, time elapsed: {}ms\".format(step, (time.time() - local_time_beg) * 1000))\n", " calculate_l2_error(model, inputs, label, config[\"train_batch_size\"])\n", " visual_result(model, inputs, label, step+1)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "poission: Derivative(u(x, y), (x, 2)) + Derivative(u(x, y), (y, 2)) + 1.0\n", " Item numbers of current derivative formula nodes: 3\n", "bc: u(x, y)\n", " Item numbers of current derivative formula nodes: 1\n", "bc_r: Derivative(u(x, y), n) - 0.5\n", " Item numbers of current derivative formula nodes: 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "loss: 1.257777\n", "step: 0, time elapsed: 7348.472833633423ms\n", " predict total time: 151.28588676452637 ms\n", " l2_error: 1.1512688311539545\n", "==================================================================================================\n", "loss: 0.492176\n", "step: 100, time elapsed: 246.30475044250488ms\n", " predict total time: 1.9807815551757812 ms\n", " l2_error: 0.7008664085681209\n", "==================================================================================================\n", "loss: 0.006177\n", "step: 200, time elapsed: 288.0725860595703ms\n", " predict total time: 2.8748512268066406 ms\n", " l2_error: 0.035529497589628596\n", "==================================================================================================\n", "loss: 0.003083\n", "step: 300, time elapsed: 276.9205570220947ms\n", " predict total time: 4.449129104614258 ms\n", " l2_error: 0.034347416303136924\n", "==================================================================================================\n", "loss: 0.002125\n", "step: 400, time elapsed: 241.45269393920898ms\n", " predict total time: 1.9965171813964844 ms\n", " l2_error: 0.024273206318798948\n", "==================================================================================================\n", "...\n", "==================================================================================================\n", "loss: 0.000126\n", "step: 4500, time elapsed: 245.61786651611328ms\n", " predict total time: 8.903980255126953 ms\n", " l2_error: 0.009561532889489787\n", "==================================================================================================\n", "loss: 0.000145\n", "step: 4600, time elapsed: 322.16882705688477ms\n", " predict total time: 7.802009582519531 ms\n", " l2_error: 0.015489169733942706\n", "==================================================================================================\n", "loss: 0.000126\n", "step: 4700, time elapsed: 212.70012855529785ms\n", " predict total time: 1.6586780548095703 ms\n", " l2_error: 0.009361597111586684\n", "==================================================================================================\n", "loss: 0.000236\n", "step: 4800, time elapsed: 215.49749374389648ms\n", " predict total time: 1.7461776733398438 ms\n", " l2_error: 0.02566272469054492\n", "==================================================================================================\n", "loss: 0.000124\n", "step: 4900, time elapsed: 256.4735412597656ms\n", " predict total time: 55.99832534790039 ms\n", " l2_error: 0.009129306458721625\n", "==================================================================================================\n", "End-to-End total time: 1209.8912012577057 s\n" ] } ], "source": [ "time_beg = time.time()\n", "train()\n", "print(\"End-to-End total time: {} s\".format(time.time() - time_beg))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 模型推理及可视化\n", "\n", "训练后可对流场内所有数据点进行推理,并可视化相关结果。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualization\n", "steps = config[\"train_steps\"]\n", "visual_result(model, inputs, label, steps+1)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.0 ('py39')", "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": "3.9.0" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "57ace93c29d9374277a79956c3f1b916d7d9a05468d906842f9921d0d494a29f" } } }, "nbformat": 4, "nbformat_minor": 2 }