Reformat code, use L-BFGS for all backends.

examples/operator/pideeponet_poisson1d.py

@@ -1,7 +1,7 @@
 """Backend supported: tensorflow.compat.v1, tensorflow, pytorch"""
-import numpy as np
 import deepxde as dde
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 # Poisson equation: -u_xx = f
@@ -26,17 +26,11 @@ def boundary(_, on_boundary):
 bc = dde.icbc.DirichletBC(geom, u_boundary, boundary)
 
 # Define PDE
-pde = dde.data.PDE(
-    geom,
-    equation,
-    bc,
-    num_domain=100,
-    num_boundary=2
-)
+pde = dde.data.PDE(geom, equation, bc, num_domain=100, num_boundary=2)
 
 # Function space for f(x) are polynomials
 degree = 3
-space = dde.data.PowerSeries(N=degree+1)
+space = dde.data.PowerSeries(N=degree + 1)
 
 # Choose evaluation points
 num_eval_points = 10
@@ -62,14 +56,9 @@ def boundary(_, on_boundary):
 
 # Define and train model
 model = dde.Model(pde_op, net)
-
-if dde.backend.backend_name == "pytorch":
-    dde.optimizers.set_LBFGS_options(maxiter=1000)
-    model.compile("L-BFGS")
-    model.train()
-else:
-    model.compile("adam", lr=0.001)
-    model.train(iterations=10000)
+dde.optimizers.set_LBFGS_options(maxiter=1000)
+model.compile("L-BFGS")
+model.train()
 
 # Plot realisations of f(x)
 n = 3
@@ -85,18 +74,18 @@ def boundary(_, on_boundary):
 plt.title("Poisson equation: Source term f(x) and solution u(x)")
 plt.ylabel("f(x)")
 z = np.zeros_like(x)
-plt.plot(x, z, 'k-', alpha=0.1)
+plt.plot(x, z, "k-", alpha=0.1)
 
 # Plot source term f(x)
 for i in range(n):
-    plt.plot(evaluation_points, fx[i], '--')
+    plt.plot(evaluation_points, fx[i], "--")
 
 # Plot solution u(x)
 plt.subplot(2, 1, 2)
 plt.ylabel("u(x)")
-plt.plot(x, z, 'k-', alpha=0.1)
+plt.plot(x, z, "k-", alpha=0.1)
 for i in range(n):
-    plt.plot(x, y[i], '-')
+    plt.plot(x, y[i], "-")
 plt.xlabel("x")
 
 plt.show()