change parameters of Lotka_Volterra example

examples/pinn_forward/Lotka_Volterra.py

@@ -3,8 +3,10 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy import integrate
+
 # Import tf if using backend tensorflow.compat.v1 or tensorflow
 from deepxde.backend import tf
+
 # Import torch if using backend pytorch
 # import torch
 # Import paddle if using backend paddle
@@ -51,6 +53,7 @@ def ode_system(x, y):
  dp_t - 1 / ub * rb * (0.02 * r * ub * p * ub - 1.06 * p * ub),
  ]
 
+
 geom = dde.geometry.TimeDomain(0, 1.0)
 data = dde.data.PDE(geom, ode_system, [], 3000, 2, num_test=3000)
 
@@ -59,6 +62,7 @@ def ode_system(x, y):
 initializer = "Glorot normal"
 net = dde.nn.FNN(layer_size, activation, initializer)
 
+
 # Backend tensorflow.compat.v1 or tensorflow
 def input_transform(t):
  return tf.concat(
@@ -73,6 +77,8 @@ def input_transform(t):
  ),
  axis=1,
  )
+
+
 # Backend pytorch
 # def input_transform(t):
 # return torch.cat(
@@ -93,20 +99,23 @@ def input_transform(t):
 # def input_transform(t):
 # if t.ndim == 1:
 # t = t[None]
-# 
+#
 # return jnp.concatenate(
 # [
 # jnp.sin(t),
 # ],
 # axis=1
 # )
 
+
 # hard constraints: x(0) = 100, y(0) = 15
 # Backend tensorflow.compat.v1 or tensorflow
 def output_transform(t, y):
  y1 = y[:, 0:1]
  y2 = y[:, 1:2]
  return tf.concat([y1 * tf.tanh(t) + 100 / ub, y2 * tf.tanh(t) + 15 / ub], axis=1)
+
+
 # Backend pytorch
 # def output_transform(t, y):
 # y1 = y[:, 0:1]
@@ -131,9 +140,9 @@ def output_transform(t, y):
 model = dde.Model(data, net)
 
 model.compile("adam", lr=0.001)
-losshistory, train_state = model.train(iterations=1000)
+losshistory, train_state = model.train(iterations=50000)
 # Most backends except jax can have a second fine tuning of the solution
-model.compile("L-BFGS", lr=0.001)
+model.compile("L-BFGS")
 losshistory, train_state = model.train()
 dde.saveplot(losshistory, train_state, issave=True, isplot=True)