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>
65 lines
2.0 KiB
Python
65 lines
2.0 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.wave import Wave
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class HardMLP(torch.nn.Module):
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def __init__(self, **kwargs):
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super().__init__()
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self.layers = FeedForward(**kwargs)
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# here in the foward we implement the hard constraints
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def forward(self, x):
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hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))
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hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t']))
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return hard_space * self.layers(x) * x.extract(['t']) + hard_t
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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("--epochs", help="extra features", type=int, default=1000)
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args = parser.parse_args()
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# create problem and discretise domain
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wave_problem = Wave()
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wave_problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])
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# create model
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model = HardMLP(
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layers=[40, 40, 40],
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output_dimensions=len(wave_problem.output_variables),
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input_dimensions=len(wave_problem.input_variables),
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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=wave_problem,
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model=model,
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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.wave'
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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=wave_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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