tmp commit - toward 0.0.1

examples/run_poisson.py

@@ -0,0 +1,70 @@
+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.poisson2D import Poisson2DProblem as Poisson2D
+from pina.deep_feed_forward import DeepFeedForward
+
+from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
+
+
+class myFeature(torch.nn.Module):
+    """
+    Feature: sin(x)
+    """
+
+    def __init__(self):
+        super(myFeature, self).__init__()
+
+    def forward(self, x):
+        return torch.sin(x[:, 0]*torch.pi) * torch.sin(x[:, 1]*torch.pi)
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="Run PINA")
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument("-s", "-save", action="store_true")
+    group.add_argument("-l", "-load", action="store_true")
+    parser.add_argument("id_run", help="number of run", type=int)
+    parser.add_argument("features", help="extra features", type=int)
+    args = parser.parse_args()
+
+    feat = [myFeature()] if args.features else []
+
+    poisson_problem = Poisson2D()
+    model = DeepFeedForward(
+        layers=[10, 10],
+        output_variables=poisson_problem.output_variables,
+        input_variables=poisson_problem.input_variables,
+        func=Softplus,
+        extra_features=feat
+    )
+
+    pinn = PINN(
+        poisson_problem,
+        model,
+        lr=0.003,
+        error_norm='mse',
+        regularizer=1e-8,
+        lr_accelerate=None)
+
+    if args.s:
+
+        pinn.span_pts(10, 'grid', ['D'])
+        pinn.span_pts(10, 'grid', ['gamma1', 'gamma2', 'gamma3', 'gamma4'])
+        #pinn.plot_pts()
+        pinn.train(10000, 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).item()))
+        pinn.save_state('pina.poisson')
+
+    else:
+        pinn.load_state('pina.poisson')
+        pinn.plot(40)
+
+