84 lines
2.4 KiB
Python
84 lines
2.4 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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