wave_pde.py

import math
import tensorflow as tf
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

tf.keras.backend.set_floatx("float64")

# Define analytic solution

# Define grid

# Define model

# Define u-function

# Define u' = v-function

# Define trial solution

# Define loss function

# Define gradient method

# Initial model and optimizer

# Run training loop
# Apply gradients in optimizer
# Output loss improvement


# Plot solution on larger grid