add stokes problem

examples/run_stokes.py

@@ -0,0 +1,54 @@
+import argparse
+import sys
+import numpy as np
+import torch
+from torch.nn import ReLU, Tanh, Softplus
+
+from pina import PINN, LabelTensor, Plotter
+from pina.model import FeedForward
+from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
+from problems.stokes import Stokes
+
+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)
+    args = parser.parse_args()
+
+
+    stokes_problem = Stokes()
+    model = FeedForward(
+        layers=[40, 20, 20, 10],
+        output_variables=stokes_problem.output_variables,
+        input_variables=stokes_problem.input_variables,
+        func=Softplus,
+    )
+
+    pinn = PINN(
+        stokes_problem,
+        model,
+        lr=0.006,
+        error_norm='mse',
+        regularizer=1e-8,
+        lr_accelerate=None)
+
+    if args.s:
+
+        #pinn.span_pts(200, 'grid', ['gamma_out'])
+        pinn.span_pts(200, 'grid', ['gamma_top', 'gamma_bot', 'gamma_in', 'gamma_out'])
+        pinn.span_pts(2000, 'random', ['D'])
+        #plotter = Plotter()
+        #plotter.plot_samples(pinn)
+        pinn.train(10000, 100)
+        pinn.save_state('pina.stokes')
+
+    else:
+        pinn.load_state('pina.stokes')
+        plotter = Plotter()
+        plotter.plot_samples(pinn)
+        plotter.plot(pinn)
+
+