ee39b39805
* modify examples/problems * modify tutorials --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-235.eduroam.sissa.it> Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
""" Run PINA on Burgers equation. """
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import argparse
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import torch
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from torch.nn import Softplus
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from pina import LabelTensor
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from pina.model import FeedForward
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from pina.solvers import PINN
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from pina.plotter import Plotter
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from pina.trainer import Trainer
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from problems.burgers import Burgers1D
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class myFeature(torch.nn.Module):
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"""
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Feature: sin(pi*x)
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"""
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def __init__(self):
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super(myFeature, self).__init__()
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def forward(self, x):
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return LabelTensor(torch.sin(torch.pi * x.extract(['x'])), ['sin(x)'])
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run PINA")
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parser.add_argument("--load", help="directory to save or load file", type=str)
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parser.add_argument("--features", help="extra features", type=int)
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parser.add_argument("--epochs", help="extra features", type=int, default=1000)
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args = parser.parse_args()
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if args.features is None:
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args.features = 0
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# extra features
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feat = [myFeature()] if args.features else []
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# create problem and discretise domain
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burgers_problem = Burgers1D()
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burgers_problem.discretise_domain(n=200, mode='grid', variables = 't', locations=['D'])
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burgers_problem.discretise_domain(n=20, mode='grid', variables = 'x', locations=['D'])
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burgers_problem.discretise_domain(n=150, mode='random', locations=['gamma1', 'gamma2', 't0'])
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# create model
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model = FeedForward(
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layers=[30, 20, 10, 5],
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output_dimensions=len(burgers_problem.output_variables),
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input_dimensions=len(burgers_problem.input_variables) + len(feat),
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func=Softplus
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)
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# create solver
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pinn = PINN(
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problem=burgers_problem,
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model=model,
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extra_features=feat,
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optimizer_kwargs={'lr' : 0.006}
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)
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# create trainer
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directory = 'pina.burger_extrafeats_{}'.format(bool(args.features))
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trainer = Trainer(solver=pinn, accelerator='cpu', max_epochs=args.epochs, default_root_dir=directory)
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if args.load:
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pinn = PINN.load_from_checkpoint(checkpoint_path=args.load, problem=burgers_problem, model=model)
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plotter = Plotter()
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plotter.plot(pinn)
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else:
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trainer.train()
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