Refactoring code

examples/problems/burgers.py

@@ -0,0 +1,49 @@
+import numpy as np
+import scipy.io
+import torch
+
+from pina.segment import Segment
+from pina.cube import Cube
+from pina.problem import TimeDependentProblem, Problem1D
+from pina.operators import grad
+
+def tmp_grad(output_, input_):
+    return torch.autograd.grad(
+            output_,
+            input_.tensor,
+            grad_outputs=torch.ones(output_.size()).to(
+                dtype=input_.tensor.dtype,
+                device=input_.tensor.device),
+            create_graph=True, retain_graph=True, allow_unused=True)[0]
+
+class Burgers1D(TimeDependentProblem, Problem1D):
+
+    input_variables = ['x', 't']
+    output_variables = ['u']
+    spatial_domain = Cube([[-1, 1]])
+    temporal_domain = Cube([[0, 1]])
+
+    def burger_equation(input_, output_):
+        grad_u = grad(output_['u'], input_)
+        grad_x, grad_t = tmp_grad(output_['u'], input_).T
+        gradgrad_u_x = grad(grad_u['x'], input_)
+        grad_xx = tmp_grad(grad_x, input_)[:, 0]
+        return grad_u['t'] + output_['u']*grad_u['x'] - (0.01/torch.pi)*gradgrad_u_x['x']
+
+
+    def nil_dirichlet(input_, output_):
+        u_expected = 0.0
+        return output_['u'] - u_expected
+
+    def initial_condition(input_, output_):
+        u_expected = -torch.sin(torch.pi*input_['x'])
+        return output_['u'] - u_expected
+
+
+
+    conditions = {
+        'gamma1': {'location': Segment((-1, 0), (-1, 1)), 'func': nil_dirichlet},
+        'gamma2': {'location': Segment(( 1, 0), ( 1, 1)), 'func': nil_dirichlet},
+        'initia': {'location': Segment((-1, 0), ( 1, 0)), 'func': initial_condition},
+        'D': {'location': Cube([[-1, 1],[0,1]]), 'func': burger_equation}
+    }