use LabelTensor, fix minor, docs

examples/problems/poisson.py

@@ -13,13 +13,14 @@ class Poisson(SpatialProblem):
     domain = Span({'x': [0, 1], 'y': [0, 1]})
 
     def laplace_equation(input_, output_):
-        force_term = (torch.sin(input_['x']*torch.pi) *
-                      torch.sin(input_['y']*torch.pi))
-        return nabla(output_['u'], input_).flatten() - force_term
+        force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
+                      torch.sin(input_.extract(['y'])*torch.pi))
+        nabla_u = nabla(output_.extract(['u']), input_)
+        return nabla_u - force_term
 
     def nil_dirichlet(input_, output_):
         value = 0.0
-        return output_['u'] - value
+        return output_.extract(['u']) - value
 
     conditions = {
         'gamma1': Condition(Span({'x': [-1, 1], 'y':  1}), nil_dirichlet),

examples/run_poisson.py

@@ -35,7 +35,7 @@ if __name__ == "__main__":
 
     poisson_problem = Poisson()
     model = FeedForward(
-        layers=[10, 10],
+        layers=[20, 20],
         output_variables=poisson_problem.output_variables,
         input_variables=poisson_problem.input_variables,
         func=Softplus,
@@ -45,17 +45,17 @@ if __name__ == "__main__":
     pinn = PINN(
         poisson_problem,
         model,
-        lr=0.003,
+        lr=0.03,
         error_norm='mse',
-        regularizer=1e-8,
-        lr_accelerate=None)
+        regularizer=1e-8)
 
     if args.s:
 
-        pinn.span_pts(20, 'grid', ['D'])
+        print(pinn)
         pinn.span_pts(20, 'grid', ['gamma1', 'gamma2', 'gamma3', 'gamma4'])
+        pinn.span_pts(20, 'grid', ['D'])
         #pinn.plot_pts()
-        pinn.train(1000, 100)
+        pinn.train(5000, 100)
         with open('poisson_history_{}_{}.txt'.format(args.id_run, args.features), 'w') as file_:
             for i, losses in enumerate(pinn.history):
                 file_.write('{} {}\n'.format(i, sum(losses)))