tmp commit - toward 0.0.1

examples/run_burgers.py

@@ -0,0 +1,115 @@
+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
+
+from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
+
+
+class myFeature(torch.nn.Module):
+    """
+    Feature: sin(x)
+    """
+
+    def __init__(self, idx):
+
+        super(myFeature, self).__init__()
+        self.idx = idx
+
+    def forward(self, x):
+
+        return torch.sin(torch.pi * x[:, self.idx])
+
+class myExp(torch.nn.Module):
+    def __init__(self, idx):
+
+        super().__init__()
+        self.idx = idx
+
+    def forward(self, x):
+
+        return torch.exp(x[:, self.idx])
+
+
+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(0)] if args.features else []
+
+    burgers_problem = Burgers1D()
+    model = DeepFeedForward(
+        layers=[20, 10, 5],
+        #layers=[8, 4, 2],
+        #layers=[16, 8, 4, 4],
+        #layers=[20, 4, 4, 4],
+        output_variables=burgers_problem.output_variables,
+        input_variables=burgers_problem.input_variables,
+        func=Tanh,
+        extra_features=feat
+    )
+
+    pinn = PINN(
+        burgers_problem,
+        model,
+        lr=0.006,
+        error_norm='mse',
+        regularizer=0,
+        lr_accelerate=None)
+
+    if args.s:
+
+        pinn.span_pts(8000, 'latin', ['D'])
+        pinn.span_pts(50, 'random', ['gamma1', 'gamma2', 'initia'])
+        #pinn.plot_pts()
+        pinn.train(10000, 1000)
+        #with open('burgers_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.burger.{}.{}'.format(args.id_run, args.features))
+
+    else:
+        pinn.load_state('pina.burger.{}.{}'.format(args.id_run, args.features))
+        #pinn.plot(256,filename='pina.burger.{}.{}.jpg'.format(args.id_run, args.features))
+
+
+        print(pinn.history)
+        with open('burgers_history_{}_{}.txt'.format(args.id_run, args.features), 'w') as file_:
+            for i, losses in enumerate(pinn.history):
+                print(losses)
+                file_.write('{} {}\n'.format(i, sum(losses)))
+        import scipy
+        data = scipy.io.loadmat('Data/burgers_shock.mat')
+        data_solution = {'grid': np.meshgrid(data['x'], data['t']), 'grid_solution': data['usol'].T}
+        import matplotlib
+        matplotlib.use('Qt5Agg')
+        import matplotlib.pyplot as plt
+
+        t =75
+        for t in [25, 50, 75]:
+            input = torch.cat([
+                    torch.linspace(-1, 1, 256).reshape(-1, 1),
+                    torch.ones(size=(256, 1)) * t /100],
+                    axis=1).double()
+            output = pinn.model(input)
+            fout = 'pina.burgers.{}.{}.t{}.dat'.format(args.id_run, args.features, t)
+            with open(fout, 'w') as f_:
+                f_.write('x utruth upinn\n')
+                for x, utruth, upinn in zip(data['x'], data['usol'][:, t], output.tensor.detach()):
+                    f_.write('{} {} {}\n'.format(x[0], utruth, upinn.item()))
+            plt.plot(data['usol'][:, t], label='truth')
+            plt.plot(output.tensor.detach(), 'x', label='pinn')
+            plt.legend()
+            plt.show()