62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import argparse
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import torch
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from torch.nn import Softplus
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from pina import Plotter
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from pina import PINN as pPINN
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from problems.parametric_poisson import ParametricPoisson
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from pina.model import FeedForward
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class myFeature(torch.nn.Module):
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"""
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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 torch.exp(- 2*(x.extract(['x']) - x.extract(['mu1']))**2 - 2*(x.extract(['y']) - x.extract(['mu2']))**2)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run PINA")
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group = parser.add_mutually_exclusive_group(required=True)
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group.add_argument("-s", "-save", action="store_true")
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group.add_argument("-l", "-load", action="store_true")
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parser.add_argument("id_run", help="number of run", type=int)
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parser.add_argument("features", help="extra features", type=int)
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args = parser.parse_args()
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feat = [myFeature()] if args.features else []
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poisson_problem = ParametricPoisson()
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model = FeedForward(
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layers=[10, 10, 10],
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output_variables=poisson_problem.output_variables,
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input_variables=poisson_problem.input_variables,
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func=Softplus,
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extra_features=feat
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)
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pinn = pPINN(
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poisson_problem,
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model,
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lr=0.0006,
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regularizer=1e-6)
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if args.s:
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pinn.span_pts(500, n_params=10, mode_spatial='random', locations=['D'])
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pinn.span_pts(200, n_params=10, mode_spatial='random', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
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pinn.plot_pts()
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pinn.train(10000, 100)
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with open('param_poisson_history_{}_{}.txt'.format(args.id_run, args.features), 'w') as file_:
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for i, losses in enumerate(pinn.history):
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file_.write('{} {}\n'.format(i, sum(losses)))
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pinn.save_state('pina.poisson_param')
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else:
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pinn.load_state('pina.poisson_param')
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plotter = Plotter()
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plotter.plot(pinn, component='u', parametric=True, params_value=0)
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