version 0.0.1

examples/run_burgers.py

@@ -1,15 +1,10 @@
-import sys
-import numpy as np
-import torch
 import argparse
-from pina.pinn import PINN
-from pina.ppinn import ParametricPINN as pPINN
-from pina.label_tensor import LabelTensor
-from torch.nn import ReLU, Tanh, Softplus
-from problems.burgers import Burgers1D
-from pina.deep_feed_forward import DeepFeedForward
+import torch
+from torch.nn import Softplus
 
-from pina import Plotter
+from pina import PINN, Plotter
+from pina.model import FeedForward
+from problems.burgers import Burgers1D
 
 
 class myFeature(torch.nn.Module):
@@ -23,6 +18,7 @@ class myFeature(torch.nn.Module):
     def forward(self, x):
         return torch.sin(torch.pi * x[:, self.idx])
 
+
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(description="Run PINA")
@@ -36,11 +32,8 @@ if __name__ == "__main__":
     feat = [myFeature(0)] if args.features else []
 
     burgers_problem = Burgers1D()
-    model = DeepFeedForward(
+    model = FeedForward(
         layers=[30, 20, 10, 5],
-        #layers=[8, 8, 8],
-        #layers=[16, 8, 4, 4],
-        #layers=[20, 4, 4, 4],
         output_variables=burgers_problem.output_variables,
         input_variables=burgers_problem.input_variables,
         func=Softplus,
@@ -57,7 +50,7 @@ if __name__ == "__main__":
 
     if args.s:
         pinn.span_pts(2000, 'latin', ['D'])
-        pinn.span_pts(150, 'random', ['gamma1', 'gamma2', 'initia'])
+        pinn.span_pts(150, 'random', ['gamma1', 'gamma2', 't0'])
         pinn.train(5000, 100)
         pinn.save_state('pina.burger.{}.{}'.format(args.id_run, args.features))
     else: