PINN解偏微分方程实例2(一维非线性薛定谔方程)-Toy模板网

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1. 一维非线性薛定谔方程

考虑偏微分方程如下：
$\begin{align} \begin{aligned} & ih_t + 0.5h_{xx} + |h|^2h = 0 \\ & h(0,x) = 2 sech(x) \\ & h(t,-5) = h(t,5) \\ & h_x(t,-5) = h_x(t,5) \end{aligned} \end{align}$
其中 $x\in[-5,5],t\in[0,\pi/2].$ 这是一个带有周期性边界条件，初始条件和复数解的偏微分方程。

2. 损失函数如下定义

$\begin{align} \begin{aligned} MSE = MSE_0 + MSE_b + MSE_f \\ \end{aligned} \end{align}$
其中
$\begin{align} \begin{aligned} MSE_0 &= \frac{1}{N_0}\sum_{i=1}^{N_0}|h(0,x_0^i)-h_0^i|^2 \\ MSE_b &= \frac{1}{N_b}\sum_{i=1}^{N_b}(|h^i(t_b^i,-5)-h^i(t_b^i,5)|^2+|h^i_x(t_b^i,-5)-h^i_x(t_b^i,5)|^2) \\ MSE_f &= \frac{1}{N_f}\sum_{i=1}^{N_f}|f(t_f^i,x_f^i)|^2 \\ \end{aligned} \end{align}$
这里 $MSE_0$ 是初始条件损失函数， $MSE_b$ 是周期条件损失函数， $MSE_f$ 是偏微分方程构造的损失函数。
由于 $h (t, x) = u (t, x) + i v (t, x)$ ，在代码实现过程中，损失函数具体形式如下：
$\begin{align} \begin{aligned} l = l_1 + l_2 + l_3 + l_4 + l_5 + l_6 + l_7 + l_8 \end{aligned} \end{align}$
其中
$\begin{align} \begin{aligned} l_1 &= \frac{1}{N_0}\sum_{i=1}^{N_0}|u(0,x_0^i)-u_0^i|^2 \\ l_2 &= \frac{1}{N_0}\sum_{i=1}^{N_0}|v(0,x_0^i)-v_0^i|^2 \\ l_3 &= \frac{1}{N_b}\sum_{i=1}^{N_b}|u^i(t_b^i,-5)-u^i(t_b^i,5)|^2 \\ l_4 &= \frac{1}{N_b}\sum_{i=1}^{N_b}|v^i(t_b^i,-5)-v^i(t_b^i,5)|^2 \\ l_5 &= \frac{1}{N_b}\sum_{i=1}^{N_b}|u^i_x(t_b^i,-5)-u^i_x(t_b^i,5)|^2 \\ l_6 &= \frac{1}{N_b}\sum_{i=1}^{N_b}|v^i_x(t_b^i,-5)-v^i_x(t_b^i,5)|^2 \\ l_7 &= \frac{1}{N_f}\sum_{i=1}^{N_f}|u_t + 0.5 *v _{xx} + (u^2+v^2)*v|^2 \\ l_8 &= \frac{1}{N_f}\sum_{i=1}^{N_f}|-v_t + 0.5 *u _{xx} + (u^2+v^2)*u|^2 \\ \end{aligned} \end{align}$
这里 $N_0=N_b=50,N_f=20000.$ 其中 $u_0^i,v_0^i$ 为谱方法计算出来的真解，其它均为神经网络输出值。

3. 代码

代码参考下图进行理解。
PINN解偏微分方程实例2(一维非线性薛定谔方程)

代码参考https://github.com/maziarraissi/PINNs,原代码运行框架tensorflow1，代码如下：

"""
@author: Maziar Raissi
@Annotator：ST
利用谱方法计算了t*x为[0,pi/2]*[-5,5]区域上的真解，真解个数t*x为201*256
"""

import sys
sys.path.insert(0, '../../Utilities/')

import tensorflow.compat.v1 as tf   # tensorflow1.0代码迁移到2.0上运行，加上这两行
tf.disable_v2_behavior()

import tensorflow as tf2
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from scipy.interpolate import griddata
from pyDOE import lhs    # 拉丁超立方采样
from plotting import newfig, savefig
from mpl_toolkits.mplot3d import Axes3D
import time
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable


np.random.seed(1234)
tf.random.set_random_seed(1234)    # tf.random.set_seed


class PhysicsInformedNN:
    # Initialize the class
    def __init__(self, x0, u0, v0, tb, X_f, layers, lb, ub):
        """
        由损失函数可以看出，周期性边界不需要真解，仅初始条件需要真解，就可以求解
        :param x0: 左边界N0个点x值
        :param u0: 左边界N0个点对应解的实部
        :param v0: 左边界N0个点对应解的虚部
        :param tb: 周期边界Nb个点t值
        :param X_f: 在区域[0,pi/2]*[-5,5]内采用拉丁超立方采样得到的Nf个点的坐标值
        :param layers: 神经网络各层神经元列表
        :param lb: np.array([-5.0, 0.0])
        :param ub: np.array([5.0, np.pi/2])
        """
        X0 = np.concatenate((x0, 0*x0), 1)  # 左边界坐标点(x,0)
        X_lb = np.concatenate((0*tb + lb[0], tb), 1)  # 下边界坐标点(-5,t)
        X_ub = np.concatenate((0*tb + ub[0], tb), 1)  # 上边界坐标点(5,t)
        
        self.lb = lb
        self.ub = ub
               
        self.x0 = X0[:,0:1]
        self.t0 = X0[:,1:2]

        self.x_lb = X_lb[:,0:1]
        self.t_lb = X_lb[:,1:2]

        self.x_ub = X_ub[:,0:1]
        self.t_ub = X_ub[:,1:2]
        
        self.x_f = X_f[:,0:1]
        self.t_f = X_f[:,1:2]
        
        self.u0 = u0
        self.v0 = v0
        
        # Initialize NNs
        self.layers = layers
        self.weights, self.biases = self.initialize_NN(layers)
        
        # tf Placeholders
        self.x0_tf = tf.placeholder(tf.float32, shape=[None, self.x0.shape[1]])    # tf.placeholder改为tf.compat.v1.placeholder
        self.t0_tf = tf.placeholder(tf.float32, shape=[None, self.t0.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        
        self.u0_tf = tf.placeholder(tf.float32, shape=[None, self.u0.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        self.v0_tf = tf.placeholder(tf.float32, shape=[None, self.v0.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        
        self.x_lb_tf = tf.placeholder(tf.float32, shape=[None, self.x_lb.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        self.t_lb_tf = tf.placeholder(tf.float32, shape=[None, self.t_lb.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        
        self.x_ub_tf = tf.placeholder(tf.float32, shape=[None, self.x_ub.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        self.t_ub_tf = tf.placeholder(tf.float32, shape=[None, self.t_ub.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        
        self.x_f_tf = tf.placeholder(tf.float32, shape=[None, self.x_f.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder
        self.t_f_tf = tf.placeholder(tf.float32, shape=[None, self.t_f.shape[1]])     # tf.placeholder改为tf.compat.v1.placeholder

        # tf Graphs
        self.u0_pred, self.v0_pred, _ , _ = self.net_uv(self.x0_tf, self.t0_tf)    # 左边界
        self.u_lb_pred, self.v_lb_pred, self.u_x_lb_pred, self.v_x_lb_pred = self.net_uv(self.x_lb_tf, self.t_lb_tf)    # 下边界
        self.u_ub_pred, self.v_ub_pred, self.u_x_ub_pred, self.v_x_ub_pred = self.net_uv(self.x_ub_tf, self.t_ub_tf)    # 上边界
        self.f_u_pred, self.f_v_pred = self.net_f_uv(self.x_f_tf, self.t_f_tf)
        
        # Loss
        self.loss = tf.reduce_mean(tf.square(self.u0_tf - self.u0_pred)) + \
                    tf.reduce_mean(tf.square(self.v0_tf - self.v0_pred)) + \
                    tf.reduce_mean(tf.square(self.u_lb_pred - self.u_ub_pred)) + \
                    tf.reduce_mean(tf.square(self.v_lb_pred - self.v_ub_pred)) + \
                    tf.reduce_mean(tf.square(self.u_x_lb_pred - self.u_x_ub_pred)) + \
                    tf.reduce_mean(tf.square(self.v_x_lb_pred - self.v_x_ub_pred)) + \
                    tf.reduce_mean(tf.square(self.f_u_pred)) + \
                    tf.reduce_mean(tf.square(self.f_v_pred))

        # MSE0|u|^2 + MSE0|v|^2 +
        # MSEb|u(-5)-u(5)|^2 + MSEb|v(-5)-v(5)|^2 +
        # MSEb|u_x(-5)-u_x(5)|^2 + MSEb|v_x(-5)-v_x(5)|^2
        # MSEf|u|^2 + MSEf|v|^2

        # 获取损失函数历史记录
        self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss,    
                                                                method = 'L-BFGS-B',
                                                                options = {'maxiter': 50000,
                                                                           'maxfun': 50000,
                                                                           'maxcor': 50,
                                                                           'maxls': 50,
                                                                           'ftol' : 1.0 * np.finfo(float).eps})
    
        self.optimizer_Adam = tf.train.AdamOptimizer()
        self.train_op_Adam = self.optimizer_Adam.minimize(self.loss)    # 反向传播算法更新权重和偏置
                
        # tf session
        self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
                                                     log_device_placement=True))
        
        init = tf.global_variables_initializer()
        self.sess.run(init)
              
    def initialize_NN(self, layers):
        """
        初始化网络权重和偏置参数
        :param layers: eg.[2, 100, 100, 100, 100, 2]
        :return:
        """
        weights = []
        biases = []
        num_layers = len(layers) 
        for l in range(0,num_layers-1):
            W = self.xavier_init(size=[layers[l], layers[l+1]])
            b = tf.Variable(tf.zeros([1,layers[l+1]], dtype=tf.float32), dtype=tf.float32)
            weights.append(W)
            biases.append(b)        
        return weights, biases
        
    def xavier_init(self, size):
        """
        正态分布初始化权重
        tf.compat.v1.random.truncated_normal(维度，正态分布均值，正态分布标准差):截断的产生正态分布的随机数，即随机数与均值的差值若大于两倍的标准差，则重新生成。
        :param size:
        :return:
        """
        in_dim = size[0]
        out_dim = size[1]        
        xavier_stddev = np.sqrt(2/(in_dim + out_dim))
        # return tf.Variable(tf.truncated_normal([in_dim, out_dim], stddev=xavier_stddev), dtype=tf.float32)
        return tf.Variable(tf.compat.v1.random.truncated_normal([in_dim, out_dim], stddev=xavier_stddev), dtype=tf.float32)

    def neural_net(self, X, weights, biases):
        """
        :param X: 输入(x,t)
        :param weights: 模型权重
        :param biases:  模型偏置
        :return: 返回(u,v),其中u为真解实部，v为真解虚部
        """
        num_layers = len(weights) + 1
        
        H = 2.0*(X - self.lb)/(self.ub - self.lb) - 1.0
        for l in range(0,num_layers-2):
            W = weights[l]
            b = biases[l]
            H = tf.tanh(tf.add(tf.matmul(H, W), b))
        W = weights[-1]
        b = biases[-1]
        Y = tf.add(tf.matmul(H, W), b)
        return Y
    
    def net_uv(self, x, t):
        X = tf.concat([x,t],1)
        
        uv = self.neural_net(X, self.weights, self.biases)
        u = uv[:,0:1]
        v = uv[:,1:2]
        
        u_x = tf.gradients(u, x)[0]
        v_x = tf.gradients(v, x)[0]

        return u, v, u_x, v_x

    def net_f_uv(self, x, t):
        u, v, u_x, v_x = self.net_uv(x,t)
        
        u_t = tf.gradients(u, t)[0]
        u_xx = tf.gradients(u_x, x)[0]
        
        v_t = tf.gradients(v, t)[0]
        v_xx = tf.gradients(v_x, x)[0]
        
        f_u = u_t + 0.5*v_xx + (u**2 + v**2)*v
        f_v = v_t - 0.5*u_xx - (u**2 + v**2)*u   
        
        return f_u, f_v
    
    def callback(self, loss):
        print('Loss:', loss)
        
    def train(self, nIter):
        
        tf_dict = {self.x0_tf: self.x0, self.t0_tf: self.t0,
                   self.u0_tf: self.u0, self.v0_tf: self.v0,
                   self.x_lb_tf: self.x_lb, self.t_lb_tf: self.t_lb,
                   self.x_ub_tf: self.x_ub, self.t_ub_tf: self.t_ub,
                   self.x_f_tf: self.x_f, self.t_f_tf: self.t_f}
        
        start_time = time.time()
        for it in range(nIter):
            self.sess.run(self.train_op_Adam, tf_dict)
            
            # Print
            if it % 10 == 0:
                elapsed = time.time() - start_time
                loss_value = self.sess.run(self.loss, tf_dict)
                print('It: %d, Loss: %.3e, Time: %.2f' % 
                      (it, loss_value, elapsed))
                start_time = time.time()
                                                                                                                          
        # self.optimizer.minimize(self.sess,
        #                         feed_dict = tf_dict,
        #                         fetches = [self.loss],
        #                         loss_callback = self.callback)
        #
    
    def predict(self, X_star):
        """

        :param X_star: 真解坐标值(x,y)
        :return:
        """
        tf_dict = {self.x0_tf: X_star[:,0:1], self.t0_tf: X_star[:,1:2]}
        
        u_star = self.sess.run(self.u0_pred, tf_dict)    # 执行sess.run()时，tensorflow并不是计算了整个图，只是计算了与想要fetch的值相关的部分。
        u_star = self.sess.run(self.v0_pred, tf_dict)    # 这里u_star、u_star是h(t,x)=u(t,x)+iv(t,x)
        
        
        tf_dict = {self.x_f_tf: X_star[:,0:1], self.t_f_tf: X_star[:,1:2]}
        
        f_u_star = self.sess.run(self.f_u_pred, tf_dict)    # 这里f_u_star是u_t + 0.5*v_xx + (u**2 + v**2)*v
        f_v_star = self.sess.run(self.f_v_pred, tf_dict)    # 这里f_v_star是v_t - 0.5*u_xx - (u**2 + v**2)*u
               
        return u_star, v_star, f_u_star, f_v_star
    
if __name__ == "__main__": 
     
    noise = 0.0        
    
    # Doman bounds
    lb = np.array([-5.0, 0.0])
    ub = np.array([5.0, np.pi/2])

    N0 = 50
    N_b = 50
    N_f = 20000
    layers = [2, 100, 100, 100, 100, 2]
        
    data = scipy.io.loadmat('../Data/NLS.mat')

    t = data['tt'].flatten()[:,None]  # (201,1)
    x = data['x'].flatten()[:,None]  # (256,1)
    Exact = data['uu']  # (256,201)
    Exact_u = np.real(Exact)  # (256,201)
    Exact_v = np.imag(Exact)  # (256,201)
    Exact_h = np.sqrt(Exact_u**2 + Exact_v**2)
    
    X, T = np.meshgrid(x,t)
    
    X_star = np.hstack((X.flatten()[:,None], T.flatten()[:,None]))
    u_star = Exact_u.T.flatten()[:,None]
    v_star = Exact_v.T.flatten()[:,None]
    h_star = Exact_h.T.flatten()[:,None]
    
    ###########################
    
    idx_x = np.random.choice(x.shape[0], N0, replace=False)  # 从0-256中选取N0个整数
    x0 = x[idx_x,:]  # (50,1)
    u0 = Exact_u[idx_x,0:1]  # (50,1)
    v0 = Exact_v[idx_x,0:1]  # (50,1)
    
    idx_t = np.random.choice(t.shape[0], N_b, replace=False)
    tb = t[idx_t,:]  # (50,1)
    
    X_f = lb + (ub-lb)*lhs(2, N_f)  # (20000,2)  # lhs(因子数，采样数)
    """
    lhs(因子数，采样数):拉丁超立方采样,若因子数为2，默认取样空间是[0,1]*[0,1]
    这里通过(-5,0)+(10,pi/2)*lhs(2, N_f)可以改变取样空间
    """
            
    model = PhysicsInformedNN(x0, u0, v0, tb, X_f, layers, lb, ub)
             
    start_time = time.time()                
    model.train(50000)    
    elapsed = time.time() - start_time                
    print('Training time: %.4f' % (elapsed))
    
        
    u_pred, v_pred, f_u_pred, f_v_pred = model.predict(X_star)
    h_pred = np.sqrt(u_pred**2 + v_pred**2)
            
    error_u = np.linalg.norm(u_star-u_pred,2)/np.linalg.norm(u_star,2)    # np.linalg.norm求2范数
    error_v = np.linalg.norm(v_star-v_pred,2)/np.linalg.norm(v_star,2)
    error_h = np.linalg.norm(h_star-h_pred,2)/np.linalg.norm(h_star,2)
    print('Error u: %e' % (error_u))
    print('Error v: %e' % (error_v))
    print('Error h: %e' % (error_h))

    
    U_pred = griddata(X_star, u_pred.flatten(), (X, T), method='cubic')    # (201,256)
    V_pred = griddata(X_star, v_pred.flatten(), (X, T), method='cubic')
    H_pred = griddata(X_star, h_pred.flatten(), (X, T), method='cubic')

    FU_pred = griddata(X_star, f_u_pred.flatten(), (X, T), method='cubic')
    FV_pred = griddata(X_star, f_v_pred.flatten(), (X, T), method='cubic')     
    

    
    ######################################################################
    ############################# Plotting ###############################
    ######################################################################    
    
    X0 = np.concatenate((x0, 0*x0), 1) 
    X_lb = np.concatenate((0*tb + lb[0], tb), 1) 
    X_ub = np.concatenate((0*tb + ub[0], tb), 1)
    X_u_train = np.vstack([X0, X_lb, X_ub])

    fig, ax = newfig(1.0, 0.9)
    ax.axis('off')
    
    ####### Row 0: h(t,x) ##################    
    gs0 = gridspec.GridSpec(1, 2)
    gs0.update(top=1-0.06, bottom=1-1/3, left=0.15, right=0.85, wspace=0)
    ax = plt.subplot(gs0[:, :])
    
    h = ax.imshow(H_pred.T, interpolation='nearest', cmap='YlGnBu', 
                  extent=[lb[1], ub[1], lb[0], ub[0]], 
                  origin='lower', aspect='auto')
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    fig.colorbar(h, cax=cax)
    
    ax.plot(X_u_train[:,1], X_u_train[:,0], 'kx', label = 'Data (%d points)' % (X_u_train.shape[0]), markersize = 4, clip_on = False)
    
    line = np.linspace(x.min(), x.max(), 2)[:,None]
    ax.plot(t[75]*np.ones((2,1)), line, 'k--', linewidth = 1)
    ax.plot(t[100]*np.ones((2,1)), line, 'k--', linewidth = 1)
    ax.plot(t[125]*np.ones((2,1)), line, 'k--', linewidth = 1)    
    
    ax.set_xlabel('$t$')
    ax.set_ylabel('$x$')
    leg = ax.legend(frameon=False, loc = 'best')
#    plt.setp(leg.get_texts(), color='w')
    ax.set_title('$|h(t,x)|$', fontsize = 10)
    
    ####### Row 1: h(t,x) slices ##################    
    gs1 = gridspec.GridSpec(1, 3)
    gs1.update(top=1-1/3, bottom=0, left=0.1, right=0.9, wspace=0.5)
    
    ax = plt.subplot(gs1[0, 0])
    ax.plot(x,Exact_h[:,75], 'b-', linewidth = 2, label = 'Exact')       
    ax.plot(x,H_pred[75,:], 'r--', linewidth = 2, label = 'Prediction')
    ax.set_xlabel('$x$')
    ax.set_ylabel('$|h(t,x)|$')    
    ax.set_title('$t = %.2f$' % (t[75]), fontsize = 10)
    ax.axis('square')
    ax.set_xlim([-5.1,5.1])
    ax.set_ylim([-0.1,5.1])
    
    ax = plt.subplot(gs1[0, 1])
    ax.plot(x,Exact_h[:,100], 'b-', linewidth = 2, label = 'Exact')       
    ax.plot(x,H_pred[100,:], 'r--', linewidth = 2, label = 'Prediction')
    ax.set_xlabel('$x$')
    ax.set_ylabel('$|h(t,x)|$')
    ax.axis('square')
    ax.set_xlim([-5.1,5.1])
    ax.set_ylim([-0.1,5.1])
    ax.set_title('$t = %.2f$' % (t[100]), fontsize = 10)
    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.8), ncol=5, frameon=False)
    
    ax = plt.subplot(gs1[0, 2])
    ax.plot(x,Exact_h[:,125], 'b-', linewidth = 2, label = 'Exact')       
    ax.plot(x,H_pred[125,:], 'r--', linewidth = 2, label = 'Prediction')
    ax.set_xlabel('$x$')
    ax.set_ylabel('$|h(t,x)|$')
    ax.axis('square')
    ax.set_xlim([-5.1,5.1])
    ax.set_ylim([-0.1,5.1])    
    ax.set_title('$t = %.2f$' % (t[125]), fontsize = 10)
    # plt.show()
    savefig('./figures/retest/reNLS')

4. 实验细节及复现结果

这里使用4层全连接神经网络，输入层和输出层各两个神经元，输入层两个神经元分别代表 $x, t$ ，输出层两个神经元分别代表 $u (x, t), v (x, t)$ ，隐藏层每层100个神经元。为了计算误差，作者提供了使用谱方法计算的 $(256 * 201)$ 个真解，其中第一维度代表空间 $x$ ，第二维度代表时间 $t$ . 训练50000次之后输出结果如下：

Loss: 2.051247e-06
Loss: 2.051247e-06
Loss: 2.051247e-06
Error u: 1.408588e-03
Error v: 1.770550e-03
Error h: 1.101867e-03

PINN解偏微分方程实例2(一维非线性薛定谔方程)

接下来是作者论文中的训练结果。
PINN解偏微分方程实例2(一维非线性薛定谔方程)

5. 可能遇到的问题

1. 若遇到以下问题，是由于matplotlib版本过高导致，具体解释见博客，我这里使用的matplotlib版本是3.4.3。
PINN解偏微分方程实例2(一维非线性薛定谔方程)
解决方法:
卸载使用虚拟环境中的matplotlib，安装matplotlib v3.4.3版本。

pip uninstall matplotlib
pip install matplotlib==3.4.3

2. 若出现cannot import name ‘newfig’ from 'plotting’错误。
解决方法:
见评论。
3. 若出现如下问题：
PINN解偏微分方程实例2(一维非线性薛定谔方程)
解决方法:

在电脑上安装texlive；
在虚拟环境上安装latex库。

参考资料

[1]. Physics-informed machine learning文章来源地址https://www.toymoban.com/news/detail-429150.html

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PINN解偏微分方程实例2(一维非线性薛定谔方程)

1. 一维非线性薛定谔方程

2. 损失函数如下定义

3. 代码

4. 实验细节及复现结果

5. 可能遇到的问题

参考资料

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