MindFlow/applications/physics_driven/navier_stokes/cylinder_flow_forward/navier_stokes2D_CN.ipynb · MindSpore/mindscience

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navier_stokes2D_CN.ipynb 148.89 KB

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# 二维圆柱绕流\n",
                "\n",
                "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/master/mindflow/zh_cn/physics_driven/mindspore_navier_stokes2D.ipynb)&emsp;[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.png)](https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/master/mindflow/zh_cn/physics_driven/mindspore_navier_stokes2D.py)&emsp;[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/mindflow/docs/source_zh_cn/physics_driven/navier_stokes2D.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*。"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## 概述\n",
                "\n",
                "圆柱绕流，即二维圆柱低速非定常绕流，流动特性与雷诺数`Re`有关。。在`Re`≤1时，流场中的惯性力与粘性力相比居次要地位，圆柱上下游的流线前后对称，阻力系数近似与`Re`成反比，此`Re`数范围的绕流称为斯托克斯区；随着Re的增大，圆柱上下游的流线逐渐失去对称性。这种特殊的现象反映了流体与物体表面相互作用的奇特本质，求解圆柱绕流则是流体力学中的经典问题。\n",
                "\n",
                "由于控制方程纳维-斯托克斯方程（Navier-Stokes equation）难以得到泛化的理论解，使用数值方法对圆柱绕流场景下控制方程进行求解，从而预测流场的流动，成为计算流体力学中的样板问题。传统求解方法通常需要对流体进行精细离散化，以捕获需要建模的现象。因此，传统有限元法（finite element method，FEM）和有限差分法（finite difference method，FDM）往往成本比较大。\n",
                "\n",
                "物理启发的神经网络方法（Physics-informed Neural Networks），以下简称`PINNs`，通过使用逼近控制方程的损失函数以及简单的网络构型，为快速求解复杂流体问题提供了新的方法。本案例利用神经网络数据驱动特性，结合`PINNs`求解圆柱绕流问题。"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## 问题描述\n",
                "\n",
                "纳维-斯托克斯方程（Navier-Stokes equation），简称`N-S`方程，是流体力学领域的经典偏微分方程，在粘性不可压缩情况下，无量纲`N-S`方程的形式如下：\n",
                "\n",
                "$$\n",
                "\\frac{\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y} = 0\n",
                "$$\n",
                "\n",
                "$$\n",
                "\\frac{\\partial u} {\\partial t} + u \\frac{\\partial u}{\\partial x} + v \\frac{\\partial u}{\\partial y} = - \\frac{\\partial p}{\\partial x} + \\frac{1} {Re} (\\frac{\\partial^2u}{\\partial x^2} + \\frac{\\partial^2u}{\\partial y^2})\n",
                "$$\n",
                "\n",
                "$$\n",
                "\\frac{\\partial v} {\\partial t} + u \\frac{\\partial v}{\\partial x} + v \\frac{\\partial v}{\\partial y} = - \\frac{\\partial p}{\\partial y} + \\frac{1} {Re} (\\frac{\\partial^2v}{\\partial x^2} + \\frac{\\partial^2v}{\\partial y^2})\n",
                "$$\n",
                "\n",
                "其中，`Re`表示雷诺数。\n",
                "\n",
                "本案例利用PINNs方法学习位置和时间到相应流场物理量的映射，实现`N-S`方程的求解：\n",
                "\n",
                "$$\n",
                "(x, y, t) \\mapsto (u, v, p)\n",
                "$$"
            ]
        },
        {
            "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. 自适应损失的多任务学习。\n",
                "4. 优化器。\n",
                "5. NavierStokes2D。\n",
                "6. 模型训练。\n",
                "7. 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",
                "\n",
                "import mindspore\n",
                "from mindspore import context, nn, ops, Tensor, jit, set_seed, load_checkpoint, load_param_into_net\n",
                "from mindspore import dtype as mstype\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "下述`src`包可以在[applications/physics_driven/navier_stokes/cylinder_flow_forward/src](https://gitee.com/mindspore/mindscience/blob/master/MindFlow/applications/physics_driven/navier_stokes/cylinder_flow_forward/src)下载。"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "from mindflow.cell import MultiScaleFCSequential\n",
                "from mindflow.loss import MTLWeightedLoss\n",
                "from mindflow.pde import NavierStokes, sympy_to_mindspore\n",
                "from mindflow.utils import load_yaml_config\n",
                "\n",
                "from src import create_training_dataset, create_test_dataset, calculate_l2_error\n",
                "\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('./configs/cylinder_flow.yaml')\n",
                "context.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\", device_id=0)\n",
                "use_ascend = context.get_context(attr_key='device_target') == \"Ascend\""
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## 创建数据集\n",
                "\n",
                "本案例对已有的雷诺数为100的标准圆柱绕流进行初始条件和边界条件数据的采样。对于训练数据集，构建平面矩形的问题域以及时间维度，再对已知的初始条件，边界条件采样；基于已有的流场中的点构造测试集。\n",
                "\n",
                "下载训练与测试数据集： [physics_driven/flow_past_cylinder/dataset](https://download.mindspore.cn/mindscience/mindflow/dataset/applications/physics_driven/flow_past_cylinder/dataset/) 。\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "./flow_past_cylinder/dataset\n",
                        "get dataset path: ./flow_past_cylinder/dataset\n",
                        "check eval dataset length: (36, 100, 50, 3)\n"
                    ]
                }
            ],
            "source": [
                "# create training dataset\n",
                "cylinder_flow_train_dataset = create_training_dataset(config)\n",
                "cylinder_dataset = cylinder_flow_train_dataset.create_dataset(batch_size=config[\"train_batch_size\"],\n",
                "                                                              shuffle=True,\n",
                "                                                              prebatched_data=True,\n",
                "                                                              drop_remainder=True)\n",
                "\n",
                "# create test dataset\n",
                "inputs, label = create_test_dataset(config[\"test_data_path\"])"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## 构建模型\n",
                "\n",
                "本示例使用一个简单的全连接网络，深度为6层，激活函数是`tanh`函数。"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": [
                "coord_min = np.array(config[\"geometry\"][\"coord_min\"] + [config[\"geometry\"][\"time_min\"]]).astype(np.float32)\n",
                "coord_max = np.array(config[\"geometry\"][\"coord_max\"] + [config[\"geometry\"][\"time_max\"]]).astype(np.float32)\n",
                "input_center = list(0.5 * (coord_max + coord_min))\n",
                "input_scale = list(2.0 / (coord_max - coord_min))\n",
                "model = MultiScaleFCSequential(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='tanh',\n",
                "                               num_scales=1,\n",
                "                               input_scale=input_scale,\n",
                "                               input_center=input_center)\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## 自适应损失的多任务学习\n",
                "\n",
                "同一时间，基于PINNs的方法需要优化多个loss，给优化过程带来的巨大的挑战。我们采用*Kendall, Alex, Yarin Gal, and Roberto Cipolla. \"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics.\" CVPR, 2018.* 论文中提出的不确定性权重算法动态调整权重。"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": [
                "mtl = MTLWeightedLoss(num_losses=cylinder_flow_train_dataset.num_dataset)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## 优化器"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [],
            "source": [
                "if config[\"load_ckpt\"]:\n",
                "    param_dict = load_checkpoint(config[\"load_ckpt_path\"])\n",
                "    load_param_into_net(model, param_dict)\n",
                "    load_param_into_net(mtl, param_dict)\n",
                "\n",
                "# define optimizer\n",
                "params = model.trainable_params() + mtl.trainable_params()\n",
                "optimizer = nn.Adam(params, config[\"optimizer\"][\"initial_lr\"])"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## NavierStokes2D\n",
                "\n",
                "下述`NavierStokes2D`将圆柱绕流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。"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [],
            "source": [
                "class NavierStokes2D(NavierStokes):\n",
                "    def __init__(self, model, re=100, loss_fn=nn.MSELoss()):\n",
                "        super(NavierStokes2D, self).__init__(model, re=re, 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 bc(self):\n",
                "        bc_u = self.u\n",
                "        bc_v = self.v\n",
                "        equations = {\"bc_u\": bc_u, \"bc_v\": bc_v}\n",
                "        return equations\n",
                "\n",
                "    def ic(self):\n",
                "        ic_u = self.u\n",
                "        ic_v = self.v\n",
                "        ic_p = self.p\n",
                "        equations = {\"ic_u\": ic_u, \"ic_v\": ic_v, \"ic_p\": ic_p}\n",
                "        return equations\n",
                "\n",
                "    def get_loss(self, pde_data, bc_data, bc_label, ic_data, ic_label):\n",
                "        pde_res = self.parse_node(self.pde_nodes, inputs=pde_data)\n",
                "        pde_residual = ops.Concat(1)(pde_res)\n",
                "        pde_loss = self.loss_fn(pde_residual, Tensor(np.array([0.0]).astype(np.float32), mstype.float32))\n",
                "\n",
                "        ic_res = self.parse_node(self.ic_nodes, inputs=ic_data)\n",
                "        ic_residual = ops.Concat(1)(ic_res)\n",
                "        ic_loss = self.loss_fn(ic_residual, ic_label)\n",
                "\n",
                "        bc_res = self.parse_node(self.bc_nodes, inputs=bc_data)\n",
                "        bc_residual = ops.Concat(1)(bc_res)\n",
                "        bc_loss = self.loss_fn(bc_residual, bc_label)\n",
                "\n",
                "        return pde_loss + ic_loss + bc_loss\n",
                "\n"
            ]
        },
        {
            "attachments": {},
            "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 = NavierStokes2D(model)\n",
                "\n",
                "    from mindspore.amp import DynamicLossScaler, auto_mixed_precision, all_finite\n",
                "    if use_ascend:\n",
                "        loss_scaler = DynamicLossScaler(1024, 2, 100)\n",
                "        auto_mixed_precision(model, 'O3')\n",
                "    else:\n",
                "        loss_scaler = None\n",
                "\n",
                "    # the loss function receives 5 data sources: pde, ic, ic_label, bc and bc_label\n",
                "    def forward_fn(pde_data, bc_data, bc_label, ic_data, ic_label):\n",
                "        loss = problem.get_loss(pde_data, bc_data, bc_label, ic_data, ic_label)\n",
                "        if use_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, bc_data, bc_label, ic_data, ic_label):\n",
                "        loss, grads = grad_fn(pde_data, bc_data, bc_label, ic_data, ic_label)\n",
                "        if use_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",
                "\n",
                "    epochs = config[\"train_epochs\"]\n",
                "    steps_per_epochs = cylinder_dataset.get_dataset_size()\n",
                "    sink_process = mindspore.data_sink(train_step, cylinder_dataset, sink_size=1)\n",
                "\n",
                "    for epoch in range(1, 1 + epochs):\n",
                "        # train\n",
                "        time_beg = time.time()\n",
                "        model.set_train(True)\n",
                "        for _ in range(steps_per_epochs + 1):\n",
                "            step_train_loss = sink_process()\n",
                "        print(f\"epoch: {epoch} train loss: {step_train_loss} epoch time: {(time.time() - time_beg)*1000 :.3f} ms\")\n",
                "        model.set_train(False)\n",
                "        if epoch % config[\"eval_interval_epochs\"] == 0:\n",
                "            # eval\n",
                "            calculate_l2_error(model, inputs, label, config)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "momentum_x: u(x, y, t)*Derivative(u(x, y, t), x) + v(x, y, t)*Derivative(u(x, y, t), y) + Derivative(p(x, y, t), x) + Derivative(u(x, y, t), t) - 0.00999999977648258*Derivative(u(x, y, t), (x, 2)) - 0.00999999977648258*Derivative(u(x, y, t), (y, 2))\n",
                        "    Item numbers of current derivative formula nodes: 6\n",
                        "momentum_y: u(x, y, t)*Derivative(v(x, y, t), x) + v(x, y, t)*Derivative(v(x, y, t), y) + Derivative(p(x, y, t), y) + Derivative(v(x, y, t), t) - 0.00999999977648258*Derivative(v(x, y, t), (x, 2)) - 0.00999999977648258*Derivative(v(x, y, t), (y, 2))\n",
                        "    Item numbers of current derivative formula nodes: 6\n",
                        "continuty: Derivative(u(x, y, t), x) + Derivative(v(x, y, t), y)\n",
                        "    Item numbers of current derivative formula nodes: 2\n",
                        "ic_u: u(x, y, t)\n",
                        "    Item numbers of current derivative formula nodes: 1\n",
                        "ic_v: v(x, y, t)\n",
                        "    Item numbers of current derivative formula nodes: 1\n",
                        "bc_u: u(x, y, t)\n",
                        "    Item numbers of current derivative formula nodes: 1\n",
                        "bc_v: v(x, y, t)\n",
                        "    Item numbers of current derivative formula nodes: 1\n",
                        "bc_p: p(x, y, t)\n",
                        "    Item numbers of current derivative formula nodes: 1\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "epoch: 100 train loss: 0.093663074 epoch time: 865.762 ms\n",
                        "    predict total time: 311.9645118713379 ms\n",
                        "    l2_error, U:  0.3021394710211443 , V:  1.000814785933711 , P:  0.7896103436562808 , Total:  0.4195581394947756\n",
                        "==================================================================================================\n",
                        "epoch: 200 train loss: 0.051423326 epoch time: 862.246 ms\n",
                        "    predict total time: 22.994279861450195 ms\n",
                        "    l2_error, U:  0.17839493992645483 , V:  1.0002689685398058 , P:  0.7346766341097746 , Total:  0.34769129318171776\n",
                        "==================================================================================================\n",
                        "epoch: 300 train loss: 0.048922822 epoch time: 862.698 ms\n",
                        "    predict total time: 20.47276496887207 ms\n",
                        "    l2_error, U:  0.19347126434977727 , V:  0.9995530930847041 , P:  0.7544902230473761 , Total:  0.35548966915028823\n",
                        "==================================================================================================\n",
                        "epoch: 400 train loss: 0.045927174 epoch time: 864.443 ms\n",
                        "    predict total time: 21.65961265563965 ms\n",
                        "    l2_error, U:  0.1824223402341706 , V:  0.9989275825381772 , P:  0.7425240152913066 , Total:  0.3495656434506572\n",
                        "==================================================================================================\n",
                        "...\n",
                        "epoch: 11600 train loss: 0.00017444199 epoch time: 865.210 ms\n",
                        "    predict total time: 24.872541427612305 ms\n",
                        "    l2_error, U:  0.014519163118953455 , V:  0.05904803878272691 , P:  0.06563451497967088 , Total:  0.023605441537703505\n",
                        "==================================================================================================\n",
                        "epoch: 11700 train loss: 0.00010273233 epoch time: 862.965 ms\n",
                        "    predict total time: 26.495933532714844 ms\n",
                        "    l2_error, U:  0.015113672755658001 , V:  0.06146986437422137 , P:  0.06977751959988018 , Total:  0.024650437825199538\n",
                        "==================================================================================================\n",
                        "epoch: 11800 train loss: 9.145654e-05 epoch time: 861.971 ms\n",
                        "    predict total time: 26.30162239074707 ms\n",
                        "    l2_error, U:  0.014403110291772709 , V:  0.056214072467378313 , P:  0.06351121097459393 , Total:  0.02285192095148332\n",
                        "==================================================================================================\n",
                        "epoch: 11900 train loss: 5.3686792e-05 epoch time: 862.390 ms\n",
                        "    predict total time: 25.954484939575195 ms\n",
                        "    l2_error, U:  0.015531273782397546 , V:  0.059835203301053276 , P:  0.07341694396502277 , Total:  0.02475477793452502\n",
                        "==================================================================================================\n",
                        "epoch: 12000 train loss: 4.5837318e-05 epoch time: 860.097 ms\n",
                        "    predict total time: 25.703907012939453 ms\n",
                        "    l2_error, U:  0.014675419283356958 , V:  0.05753859917060074 , P:  0.06372057740590953 , Total:  0.02328489716397064\n",
                        "==================================================================================================\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": {
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",
                        "text/plain": [
                            "<Figure size 1920x1440 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "from src import visual\n",
                "\n",
                "# visualization\n",
                "visual(model=model, epochs=config[\"train_epochs\"], input_data=inputs, label=label)"
            ]
        }
    ],
    "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
}

一键复制编辑原始数据按行查看历史

提交于 2023-09-03 22:34 . fix link and format

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