我已经根据Maziar Raissi（PINNs）编写的代码为一维ODE编写了代码。网络的近似解决方案与精确解不匹配。我无法解决问题。这是代码
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from scipy.interpolate import griddata
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.gridspec as gridspec
import time
np.random.seed(1234)
tf.set_random_seed(1234)
class ODE:
# Initialize the class
def __init__(self, x, layers):
self.x = x
self.layers = layers
# Initialize NNs
self.weights, self.biases = self.initialize_NN(layers)
# tf placeholders and graph
self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True))
# Initialize parameters
self.x_tf = tf.placeholder(tf.float32)
self.d_g_t = self.d_g_t(self.x_tf)
self.func = self.func(self.x_tf)
self.loss = tf.reduce_mean(tf.square(self.d_g_t - self.func))
self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss,
method = 'L-BFGS-B',
options = {'maxiter': 5000,
'maxfun': 50000,
'maxcor': 50,
'ftol' : 1.0 * np.finfo(float).eps})
self.optimizer_Adam = tf.train.AdamOptimizer()
self.train_op_Adam = self.optimizer_Adam.minimize(self.loss)
init = tf.global_variables_initializer()
self.sess.run(init)