{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Navier-Stokes方程反问题\n",
    "\n",
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/zh_cn/physics_driven/mindspore_navier_stokes_inverse.ipynb) [![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/zh_cn/physics_driven/mindspore_navier_stokes_inverse.py) [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindflow/docs/source_zh_cn/physics_driven/navier_stokes_inverse.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",
    "Navier-Stokes方程是一组描述流体力学中流体速度和压力变化的偏微分方程。Navier-Stokes的反问题是指，在已知某些流体运动特征（如流量、速度等）的条件下，求解出能够产生这些运动特征的流体性质（如黏度、密度等）和流体边界条件（如壁面摩擦力等）的问题。与正问题（即已知流体性质和边界条件，求解流体的运动特征）不同，反问题的解决需要通过数值优化和逆推算法等方法进行求解。\n",
    "\n",
    "Navier-Stokes的反问题在工程和科学计算中具有广泛的应用，例如在航空、能源、地质、生物等领域中，可以用于优化流体设计、预测流体运动、诊断流体问题等。尽管Navier-Stokes的反问题非常具有挑战性，但是近年来随着计算机技术和数值方法的发展，已经取得了一定的进展。例如，可以通过使用高性能计算和基于机器学习的逆推算法等技术，加速反问题的求解过程，并提高求解精度。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题描述\n",
    "\n",
    "与Navier-Stokes方程不同的是，在Navier-Stokes方程反问题中，存在两个未知的参数。Navier-Stokes方程的反问题形式如下：\n",
    "\n",
    "$$\n",
    "\\frac{\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y} = 0\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\frac{\\partial u} {\\partial t} + \\theta_1 (u \\frac{\\partial u}{\\partial x} + v \\frac{\\partial u}{\\partial y}) = - \\frac{\\partial p}{\\partial x} + \\theta_2 (\\frac{\\partial^2u}{\\partial x^2} + \\frac{\\partial^2u}{\\partial y^2})\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\frac{\\partial v} {\\partial t} + \\theta_1 (u \\frac{\\partial v}{\\partial x} + v \\frac{\\partial v}{\\partial y}) = - \\frac{\\partial p}{\\partial y} + \\theta_2 (\\frac{\\partial^2v}{\\partial x^2} + \\frac{\\partial^2v}{\\partial y^2})\n",
    "$$\n",
    "\n",
    "其中 $\\theta 1$ 和 $\\theta 2$ 是未知的参数。\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，求解两个参数。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 技术路径\n",
    "\n",
    "MindSpore Flow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. InvNavierStokes。\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",
    "\n",
    "import mindspore\n",
    "from mindspore import context, nn, ops, jit, set_seed, load_checkpoint, load_param_into_net"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下述 `src` 包可以在[applications/physics_driven/navier_stokes/cylinder_flow_inverse/src](https://gitee.com/mindspore/mindscience/tree/master/MindFlow/applications/physics_driven/navier_stokes/cylinder_flow_inverse/src) 下载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindflow.cell import MultiScaleFCSequential\n",
    "from mindflow.utils import load_yaml_config\n",
    "from mindflow.pde import sympy_to_mindspore, PDEWithLoss\n",
    "from mindflow.loss import get_loss_metric\n",
    "\n",
    "from src import create_training_dataset, create_test_dataset, calculate_l2_error\n",
    "\n",
    "set_seed(123456)\n",
    "np.random.seed(123456)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下述`inverse_navier_stokes.yaml`文件可以在[applications/physics_driven/navier_stokes/cylinder_flow_inverse/configs/navier_stokes_inverse.yaml](https://gitee.com/mindspore/mindscience/blob/master/MindFlow/applications/physics_driven/navier_stokes/cylinder_flow_inverse/configs/navier_stokes_inverse.yaml)下载。"
   ]
  },
  {
   "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('inverse_navier_stokes.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",
    "在本案例中，训练数据和测试数据均从[原数据](https://github.com/maziarraissi/PINNs/blob/master/main/Data/cylinder_nektar_wake.mat)中取样得到。\n",
    "\n",
    "下载训练与测试数据集：[physics_driven/inverse_navier_stokes/dataset](https://download.mindspore.cn/mindscience/mindflow/dataset/applications/physics_driven/inverse_navier_stokes/dataset)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./inverse_navier_stokes/dataset\n",
      "get dataset path: ./inverse_navier_stokes/dataset\n",
      "check eval dataset length: (40, 100, 50, 3)\n"
     ]
    }
   ],
   "source": [
    "# create dataset\n",
    "inv_ns_train_dataset = create_training_dataset(config)\n",
    "train_dataset = inv_ns_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(config)"
   ]
  },
  {
   "attachments": {},
   "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，每层有20个神经元。"
   ]
  },
  {
   "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",
    "\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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 优化器\n",
    "\n",
    "在构建优化器时，两个未知参数与模型的参数一起放入优化器中训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
    "\n",
    "theta = mindspore.Parameter(mindspore.Tensor(np.array([0.0, 0.0]).astype(np.float32)), name=\"theta\", requires_grad=True)\n",
    "params = model.trainable_params()\n",
    "params.append(theta)\n",
    "optimizer = nn.Adam(params, learning_rate=config[\"optimizer\"][\"initial_lr\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## InvNavierStokes\n",
    "\n",
    "下述`inv_Navier_Stokes` 方程定义了这个反问题，包含两个部分：控制方程和训练数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import diff, Function, symbols\n",
    "from mindspore import numpy as mnp\n",
    "\n",
    "class InvNavierStokes(PDEWithLoss):\n",
    "    r\"\"\"\n",
    "    2D inverse NavierStokes equation problem based on PDEWithLoss.\n",
    "\n",
    "    Args:\n",
    "        model (mindspore.nn.Cell): network for training.\n",
    "        params(mindspore.Tensor): parameter needs training\n",
    "        loss_fn (Union[str, Cell]): Define the loss function. Default: mse.\n",
    "\n",
    "    Supported Platforms:\n",
    "        ``Ascend`` ``GPU``\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model, params, loss_fn=\"mse\"):\n",
    "\n",
    "        self.params_val = params[-1]\n",
    "        self.theta1, self.theta2 = symbols('theta1 theta2')\n",
    "        self.x, self.y, self.t = symbols('x y t')\n",
    "        self.u = Function('u')(self.x, self.y, self.t)\n",
    "        self.v = Function('v')(self.x, self.y, self.t)\n",
    "        self.p = Function('p')(self.x, self.y, self.t)\n",
    "\n",
    "        self.in_vars = [self.x, self.y, self.t]\n",
    "        self.out_vars = [self.u, self.v, self.p]\n",
    "        self.params = [self.theta1, self.theta2]\n",
    "\n",
    "        super(InvNavierStokes, self).__init__(model, self.in_vars, self.out_vars, self.params, self.params_val)\n",
    "        self.data_nodes = sympy_to_mindspore(self.data_loss(), self.in_vars, self.out_vars)\n",
    "        if isinstance(loss_fn, str):\n",
    "            self.loss_fn = get_loss_metric(loss_fn)\n",
    "        else:\n",
    "            self.loss_fn = loss_fn\n",
    "\n",
    "    def pde(self):\n",
    "        \"\"\"\n",
    "        Define governing equations based on sympy, abstract method.\n",
    "\n",
    "        Returns:\n",
    "            dict, user defined sympy symbolic equations.\n",
    "        \"\"\"\n",
    "        momentum_x = self.u.diff(self.t) + \\\n",
    "                     self.theta1 * (self.u * self.u.diff(self.x) + self.v * self.u.diff(self.y)) + \\\n",
    "                     self.p.diff(self.x) - \\\n",
    "                     self.theta2 * (diff(self.u, (self.x, 2)) + diff(self.u, (self.y, 2)))\n",
    "        momentum_y = self.v.diff(self.t) + \\\n",
    "                     self.theta1 * (self.u * self.v.diff(self.x) + self.v * self.v.diff(self.y)) + \\\n",
    "                     self.p.diff(self.y) - \\\n",
    "                     self.theta2 * (diff(self.v, (self.x, 2)) + diff(self.v, (self.y, 2)))\n",
    "        continuty = self.u.diff(self.x) + self.v.diff(self.y)\n",
    "\n",
    "        equations = {\"momentum_x\": momentum_x, \"momentum_y\": momentum_y, \"continuty\": continuty}\n",
    "        return equations\n",
    "\n",
    "    def data_loss(self):\n",
    "        \"\"\"\n",
    "        Define governing equations based on sympy, abstract method.\n",
    "\n",
    "        Returns:\n",
    "            dict, user defined sympy symbolic equations.\n",
    "        \"\"\"\n",
    "        velocity_u = self.u\n",
    "        velocity_v = self.v\n",
    "        p = self.p\n",
    "        equations = {\"velocity_u\": velocity_u, \"velocity_v\": velocity_v, \"p\": p}\n",
    "        return equations\n",
    "\n",
    "    def get_loss(self, pde_data, data, label):\n",
    "        \"\"\"\n",
    "        loss contains 2 parts,pde parts and data loss.\n",
    "        \"\"\"\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, mnp.zeros_like(pde_residual))\n",
    "\n",
    "        data_res = self.parse_node(self.data_nodes, inputs=data)\n",
    "        data_residual = ops.Concat(1)(data_res)\n",
    "        train_data_loss = self.loss_fn(data_residual, label)\n",
    "\n",
    "        return pde_loss + train_data_loss"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train():\n",
    "    problem = InvNavierStokes(model, params)\n",
    "    if use_ascend:\n",
    "        from mindspore.amp import DynamicLossScaler, auto_mixed_precision, all_finite\n",
    "        loss_scaler = DynamicLossScaler(1024, 2, 100)\n",
    "        auto_mixed_precision(model, 'O3')\n",
    "\n",
    "    def forward_fn(pde_data, train_points, train_label):\n",
    "        loss = problem.get_loss(pde_data, train_points, train_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",
    "    @jit\n",
    "    def train_step(pde_data, train_points, train_label):\n",
    "        loss, grads = grad_fn(pde_data, train_points, train_label)\n",
    "        if use_ascend:\n",
    "            loss = loss_scaler.unscale(loss)\n",
    "            if all_finite(grads):\n",
    "                grads = loss_scaler.unscale(grads)\n",
    "                loss = ops.depend(loss, optimizer(grads))\n",
    "        else:\n",
    "            loss = ops.depend(loss, optimizer(grads))\n",
    "        return loss\n",
    "\n",
    "    epochs = config[\"train_epochs\"]\n",
    "    steps_per_epochs = train_dataset.get_dataset_size()\n",
    "    sink_process = mindspore.data_sink(train_step, train_dataset, sink_size=1)\n",
    "\n",
    "    param1_hist = []\n",
    "    param2_hist = []\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):\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",
    "        # if epoch % 10 ==0:\n",
    "            print(f\"Params are{params[-1].value()}\")\n",
    "            param1_hist.append(params[-1].value()[0])\n",
    "            param2_hist.append(params[-1].value()[1])\n",
    "            calculate_l2_error(model, inputs, label, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "momentum_x: theta1*u(x, y, t)*Derivative(u(x, y, t), x) + theta1*v(x, y, t)*Derivative(u(x, y, t), y) - theta2*Derivative(u(x, y, t), (x, 2)) - theta2*Derivative(u(x, y, t), (y, 2)) + Derivative(p(x, y, t), x) + Derivative(u(x, y, t), t)\n",
      "    Item numbers of current derivative formula nodes: 6\n",
      "momentum_y: theta1*u(x, y, t)*Derivative(v(x, y, t), x) + theta1*v(x, y, t)*Derivative(v(x, y, t), y) - theta2*Derivative(v(x, y, t), (x, 2)) - theta2*Derivative(v(x, y, t), (y, 2)) + Derivative(p(x, y, t), y) + Derivative(v(x, y, t), t)\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",
      "velocity_u: u(x, y, t)\n",
      "    Item numbers of current derivative formula nodes: 1\n",
      "velocity_v: v(x, y, t)\n",
      "    Item numbers of current derivative formula nodes: 1\n",
      "p: p(x, y, t)\n",
      "    Item numbers of current derivative formula nodes: 1\n",
      "epoch: 100 train loss: 0.04028289 epoch time: 2229.404 ms\n",
      "Params are[-0.09575531  0.00106778]\n",
      "    predict total time: 652.7423858642578 ms\n",
      "    l2_error, U:  0.17912744544874443 , V:  1.0003739133641472 , P:  0.7065071079210473 , Total:  0.3464922229721188\n",
      "==================================================================================================\n",
      "epoch: 200 train loss: 0.036820486 epoch time: 1989.347 ms\n",
      "Params are[-0.09443577  0.00024772]\n",
      "    predict total time: 205.59978485107422 ms\n",
      "    l2_error, U:  0.17490993243690725 , V:  1.0000642916277709 , P:  0.7122513574462407 , Total:  0.3447007384838206\n",
      "==================================================================================================\n",
      "epoch: 300 train loss: 0.038683612 epoch time: 2111.895 ms\n",
      "Params are[-0.08953815  0.00040177]\n",
      "    predict total time: 222.2282886505127 ms\n",
      "    l2_error, U:  0.17713679346188396 , V:  1.000105316860139 , P:  0.718292536660169 , Total:  0.34596943008999737\n",
      "==================================================================================================\n",
      "Params are[-0.08414971  0.00108538]\n",
      "    predict total time: 183.55488777160645 ms\n",
      "    l2_error, U:  0.17421055947839162 , V:  1.0021844256262489 , P:  0.7172107774589106 , Total:  0.34508045682750343\n",
      "... ...\n",
      "epoch: 9700 train loss: 0.00012139356 epoch time: 1816.661 ms\n",
      "Params are[0.9982705  0.01092069]\n",
      "    predict total time: 126.16825103759766 ms\n",
      "    l2_error, U:  0.010509542864484654 , V:  0.023635759087951035 , P:  0.029506766787788633 , Total:  0.012708719069239902\n",
      "==================================================================================================\n",
      "epoch: 9800 train loss: 0.00013328926 epoch time: 1790.342 ms\n",
      "Params are[0.9984688  0.01081933]\n",
      "    predict total time: 121.35672569274902 ms\n",
      "    l2_error, U:  0.009423471629415427 , V:  0.034303037318821124 , P:  0.03254805487768195 , Total:  0.01400324770722587\n",
      "==================================================================================================\n",
      "epoch: 9900 train loss: 0.00013010873 epoch time: 1805.052 ms\n",
      "Params are[0.9978893  0.01079915]\n",
      "    predict total time: 125.68926811218262 ms\n",
      "     l2_error, U:  0.009608467956285763 , V:  0.02630037021521753 , P:  0.029346654211944684 , Total:  0.012485673337358347\n",
      "==================================================================================================\n",
      "epoch: 10000 train loss: 0.00010107973 epoch time: 1809.868 ms\n",
      "Params are[0.9984444  0.01072927]\n",
      "    predict total time: 132.6577663421631 ms\n",
      "    l2_error, U:  0.008157992439025805 , V:  0.022894215240181922 , P:  0.028113136535916225 , Total:  0.010848400336992805\n",
      "==================================================================================================\n",
      "End-to-End total time: 27197.875846385956 s\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "train()\n",
    "print(\"End-to-End total time: {} s\".format(time.time() - start_time))"
   ]
  },
  {
   "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": 2,
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'value')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src import plot_params\n",
    "\n",
    "plot_params(param1_hist, param2_hist)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本案例中，$\\theta_1$ 和 $\\theta_2$ 的标准值分别为1和0.01。\n",
    "\n",
    "Correct PDE:\n",
    "\n",
    "- $u_t + (u u_x + v u_x) = - p_x + 0.01(u_{xx} + u_{yy})$\n",
    "\n",
    "- $v_t + (u v_x + v v_x) = - p_y + 0.01(v_{xx} + v_{yy})$\n",
    "\n",
    "Identified PDE:\n",
    "\n",
    "- $u_t + 0.9984444(u u_x + v u_x) = - p_x + 0.01072927(u_{xx} + u_{yy})$\n",
    "\n",
    "- $v_t + 0.9984444(u v_x + v v_x) = - p_y + 0.01072927(v_{xx} + v_{yy})$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mindspore_py37",
   "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": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}