84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
# import argparse
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# import numpy as np
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# import torch
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# from torch.nn import Softplus
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# from pina import PINN, LabelTensor, Plotter
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# from pina.model import MultiFeedForward
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# from problems.parametric_elliptic_optimal_control_alpha_variable import (
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# ParametricEllipticOptimalControl)
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# class myFeature(torch.nn.Module):
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# """
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# Feature: sin(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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# t = (-x.extract(['x1'])**2+1) * (-x.extract(['x2'])**2+1)
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# return LabelTensor(t, ['k0'])
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# class CustomMultiDFF(MultiFeedForward):
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# def __init__(self, dff_dict):
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# super().__init__(dff_dict)
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# def forward(self, x):
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# out = self.uu(x)
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# p = LabelTensor((out.extract(['u_param']) * x.extract(['alpha'])), ['p'])
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# return out.append(p)
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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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# args = parser.parse_args()
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# opc = ParametricEllipticOptimalControl()
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# model = CustomMultiDFF(
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# {
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# 'uu': {
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# 'input_variables': ['x1', 'x2', 'mu', 'alpha'],
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# 'output_variables': ['u_param', 'y'],
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# 'layers': [40, 40, 20],
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# 'func': Softplus,
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# 'extra_features': [myFeature()],
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# },
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# }
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# )
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# pinn = PINN(
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# opc,
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# model,
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# lr=0.002,
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# error_norm='mse',
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# regularizer=1e-8)
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# if args.s:
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# pinn.span_pts(
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# {'variables': ['x1', 'x2'], 'mode': 'random', 'n': 100},
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# {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
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# locations=['D'])
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# pinn.span_pts(
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# {'variables': ['x1', 'x2'], 'mode': 'grid', 'n': 20},
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# {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
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# locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
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# pinn.train(1000, 20)
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# pinn.save_state('pina.ocp')
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# else:
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# pinn.load_state('pina.ocp')
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# plotter = Plotter()
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# plotter.plot(pinn, components='y', fixed_variables={'alpha': 0.01, 'mu': 1.0})
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# plotter.plot(pinn, components='u_param', fixed_variables={'alpha': 0.01, 'mu': 1.0})
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# plotter.plot(pinn, components='p', fixed_variables={'alpha': 0.01, 'mu': 1.0})
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raise NotImplementedError('not available problem at the moment...') |