{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 一维Burgers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_burgers1D.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_burgers1D.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/burgers1D.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*。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 概述\n", "\n", "计算流体力学是21世纪流体力学领域的重要技术之一,其通过使用数值方法在计算机中对流体力学的控制方程进行求解,从而实现流动的分析、预测和控制。传统的有限元法(finite element method,FEM)和有限差分法(finite difference method,FDM)常囿于复杂的仿真流程(物理建模,网格划分,数值离散,迭代求解等)和较高的计算成本,往往效率低下。因此,借助AI提升流体仿真效率是十分必要的。\n", "\n", "在经典理论与结合计算机性能的数值求解方法的发展趋于平缓的时候,近年来机器学习方法通过神经网络结合大量数据,实现流场的快速仿真,获得了接近传统方法的求解精度,为流场求解提供了新思路。\n", "\n", "伯格斯方程(Burgers' equation)是一个模拟冲击波的传播和反射的非线性偏微分方程,被广泛应用于流体力学,非线性声学,气体动力学等领域,它以约翰内斯·马丁斯汉堡(1895-1981)的名字命名。本案例采用MindFlow流体仿真套件,基于物理驱动的PINNs (Physics Informed Neural Networks)方法,求解一维有粘性情况下的Burgers方程。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 问题描述\n", "\n", "Burgers'方程的形式如下:\n", "\n", "$$\n", "u_t + uu_x = \\epsilon u_{xx}, \\quad x \\in[-1,1], t \\in[0, T],\n", "$$\n", "\n", "其中$\\epsilon=0.01/\\pi$,等号左边为对流项,右边为耗散项,本案例使用迪利克雷边界条件和正弦函数的初始条件,形式如下:\n", "\n", "$$\n", "u(t, -1) = u(t, 1) = 0,\n", "$$\n", "\n", "$$\n", "u(0, x) = -sin(\\pi x),\n", "$$\n", "\n", "本案例利用PINNs方法学习位置和时间到相应物理量的映射$(x, t) \\mapsto u$,实现Burgers'方程的求解。" ] }, { "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. Burgers1D。\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", "\n", "import numpy as np\n", "import sympy\n", "\n", "import mindspore\n", "from mindspore import context, nn, ops, Tensor, jit, set_seed\n", "from mindspore import dtype as mstype\n", "from mindspore import load_checkpoint, load_param_into_net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下述`src`包可以在[applications/physics_driven/burgers_pinns/src](https://gitee.com/mindspore/mindscience/tree/r0.2.0-alpha/MindFlow/applications/physics_driven/burgers_pinns/src)下载。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from mindflow.pde import Burgers, 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, visual_result, 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", "context.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\", device_id=4)\n", "is_ascend = context.get_context(attr_key='device_target') == \"Ascend\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# load configurations\n", "config = load_yaml_config('burgers_cfg.yaml')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 创建数据集\n", "\n", "本案例根据求解域、初始条件及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:\n", "\n", "下载测试数据集: [physics_driven/burgers_pinns/dataset](https://download.mindspore.cn/mindscience/mindflow/dataset/applications/physics_driven/burgers_pinns/dataset/) 。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# create training dataset\n", "burgers_train_dataset = create_training_dataset(config)\n", "train_dataset = burgers_train_dataset.create_dataset(batch_size=config[\"train_batch_size\"],\n", " shuffle=True,\n", " prebatched_data=True,\n", " drop_remainder=True)\n", "# create test dataset\n", "inputs, label = create_test_dataset()" ] }, { "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,激发函数为`tanh`函数。" ] }, { "cell_type": "code", "execution_count": 6, "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)\n", "if config[\"load_ckpt\"]:\n", " param_dict = load_checkpoint(config[\"load_ckpt_path\"])\n", " load_param_into_net(model, param_dict)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 优化器" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# define optimizer\n", "optimizer = nn.Adam(model.trainable_params(), config[\"optimizer\"][\"initial_lr\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Burgers1D\n", "\n", "下述`Burgers1D`将Burgers 1-D问题同数据集关联起来,包含3个部分:控制方程,边界条件和初始条件。\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class Burgers1D(Burgers):\n", " def __init__(self, model, loss_fn=nn.MSELoss()):\n", " super(Burgers1D, self).__init__(model, loss_fn=loss_fn)\n", " self.ic_nodes = sympy_to_mindspore(self.ic(), self.in_vars, self.out_vars)\n", " self.bc_nodes = sympy_to_mindspore(self.bc(), self.in_vars, self.out_vars)\n", "\n", " def ic(self):\n", " ic_eq = self.u + sympy.sin(np.pi * self.x)\n", " equations = {\"ic\": ic_eq}\n", " return equations\n", "\n", " def bc(self):\n", " bc_eq = self.u\n", " equations = {\"bc\": bc_eq}\n", " return equations\n", "\n", " def get_loss(self, pde_data, ic_data, bc_data):\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", " ic_res = self.parse_node(self.ic_nodes, inputs=ic_data)\n", " ic_loss = self.loss_fn(ic_res[0], Tensor(np.array([0.0]), mstype.float32))\n", "\n", " bc_res = self.parse_node(self.bc_nodes, inputs=bc_data)\n", " bc_loss = self.loss_fn(bc_res[0], Tensor(np.array([0.0]), mstype.float32))\n", "\n", " return pde_loss + ic_loss + bc_loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 模型训练\n", "\n", "使用**MindSpore >= 2.0.0**的版本,可以使用函数式编程范式训练神经网络。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def train():\n", " problem = Burgers1D(model)\n", "\n", " from mindspore.amp import DynamicLossScaler, auto_mixed_precision, all_finite\n", " if is_ascend:\n", " loss_scaler = DynamicLossScaler(1024, 2, 100)\n", " auto_mixed_precision(model, 'O1')\n", " else:\n", " loss_scaler = None\n", "\n", " # the loss function receives 3 data sources: pde, ic and bc\n", " def forward_fn(pde_data, ic_data, bc_data):\n", " loss = problem.get_loss(pde_data, ic_data, bc_data)\n", " if is_ascend:\n", " loss = loss_scaler.scale(loss)\n", " return loss\n", "\n", " grad_fn = ops.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=False)\n", "\n", " # using jit function to accelerate training process\n", " @jit\n", " def train_step(pde_data, ic_data, bc_data):\n", " loss, grads = grad_fn(pde_data, ic_data, bc_data)\n", " if is_ascend:\n", " loss = loss_scaler.unscale(loss)\n", " if all_finite(grads):\n", " grads = loss_scaler.unscale(grads)\n", "\n", " loss = ops.depend(loss, optimizer(grads))\n", " return loss\n", "\n", " steps = config[\"train_steps\"]\n", " sink_process = mindspore.data_sink(train_step, train_dataset, sink_size=1)\n", " model.set_train()\n", " for step in range(steps + 1):\n", " 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() - time_beg)*1000))\n", " calculate_l2_error(model, inputs, label, config[\"train_batch_size\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "burgers: u(x, t)*Derivative(u(x, t), x) + Derivative(u(x, t), t) - 0.00318309897556901*Derivative(u(x, t), (x, 2))\n", " Item numbers of current derivative formula nodes: 3\n", "ic: u(x, t) + sin(3.14159265358979*x)\n", " Item numbers of current derivative formula nodes: 2\n", "bc: u(x, t)\n", " Item numbers of current derivative formula nodes: 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "loss: 0.496386\n", "step: 0, time elapsed: 6659.432411193848ms\n", " predict total time: 321.7020034790039 ms\n", " l2_error: 0.9996012634029987\n", "==================================================================================================\n", "loss: 0.430037\n", "step: 100, time elapsed: 52.46543884277344ms\n", " predict total time: 7.758617401123047 ms\n", " l2_error: 0.8785584161729442\n", "==================================================================================================\n", "loss: 0.419507\n", "step: 200, time elapsed: 52.703857421875ms\n", " predict total time: 9.288311004638672 ms\n", " l2_error: 0.8896571207319739\n", "==================================================================================================\n", "loss: 0.421943\n", "step: 300, time elapsed: 52.28066444396973ms\n", " predict total time: 10.43701171875 ms\n", " l2_error: 0.8894440504950664\n", "==================================================================================================\n", "loss: 0.424456\n", "step: 400, time elapsed: 53.4367561340332ms\n", " predict total time: 9.062528610229492 ms\n", " l2_error: 0.8890160240749762\n", "==================================================================================================\n", "loss: 0.425506\n", "step: 500, time elapsed: 53.04861068725586ms\n", " predict total time: 10.000944137573242 ms\n", " l2_error: 0.8880668995398232\n", "==================================================================================================\n", "...\n", "==================================================================================================\n", "loss: 0.000106\n", "step: 14000, time elapsed: 51.543235778808594ms\n", " predict total time: 5.096197128295898 ms\n", " l2_error: 0.008158178586820691\n", "==================================================================================================\n", "loss: 0.000138\n", "step: 14100, time elapsed: 52.14524269104004ms\n", " predict total time: 8.270502090454102 ms\n", " l2_error: 0.007805042459243015\n", "==================================================================================================\n", "loss: 0.000241\n", "step: 14200, time elapsed: 52.43253707885742ms\n", " predict total time: 7.838010787963867 ms\n", " l2_error: 0.004813975769710184\n", "==================================================================================================\n", "loss: 0.002428\n", "step: 14300, time elapsed: 52.78778076171875ms\n", " predict total time: 6.4067840576171875 ms\n", " l2_error: 0.06407312413263815\n", "==================================================================================================\n", "loss: 0.000141\n", "step: 14400, time elapsed: 52.76918411254883ms\n", " predict total time: 6.978273391723633 ms\n", " l2_error: 0.012647436530672565\n", "==================================================================================================\n", "loss: 0.000082\n", "step: 14500, time elapsed: 51.911115646362305ms\n", " predict total time: 5.313634872436523 ms\n", " l2_error: 0.0047564595594806035\n", "==================================================================================================\n", "loss: 0.000081\n", "step: 14600, time elapsed: 52.56342887878418ms\n", " predict total time: 8.41522216796875 ms\n", " l2_error: 0.005077659280011354\n", "==================================================================================================\n", "loss: 0.000099\n", "step: 14700, time elapsed: 52.515506744384766ms\n", " predict total time: 8.713960647583008 ms\n", " l2_error: 0.0049527912578844506\n", "==================================================================================================\n", "loss: 0.000224\n", "step: 14800, time elapsed: 51.94854736328125ms\n", " predict total time: 7.274150848388672 ms\n", " l2_error: 0.0055557865591330845\n", "==================================================================================================\n", "loss: 0.000080\n", "step: 14900, time elapsed: 52.850961685180664ms\n", " predict total time: 8.992195129394531 ms\n", " l2_error: 0.004695746950148064\n", "==================================================================================================\n", "loss: 0.000149\n", "step: 15000, time elapsed: 51.58638954162598ms\n", " predict total time: 4.684686660766602 ms\n", " l2_error: 0.004412906530960828\n", "==================================================================================================\n", "End-to-End total time: 789.5434384346008 s\n" ] } ], "source": [ "time_beg = time.time()\n", "train()\n", "print(\"End-to-End total time: {} s\".format(time.time() - time_beg))" ] }, { "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": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualization\n", "steps = config[\"train_steps\"]\n", "visual_result(model, step=steps, resolution=config[\"visual_resolution\"])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.0 64-bit", "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": "3.8.0" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "df0893f56f349688326838aaeea0de204df53a132722cbd565e54b24a8fec5f6" } } }, "nbformat": 4, "nbformat_minor": 2 }