74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
import argparse
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import torch
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from torch.nn import ReLU, Tanh, Softplus, PReLU
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from pina.problem import SpatialProblem, ParametricProblem
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from pina.operators import nabla, grad, div
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from pina.model import FeedForward, DeepONet
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from pina import Condition, Span, LabelTensor, Plotter, PINN
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import matplotlib
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matplotlib.use('Qt5Agg')
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class FirstOrderODE(SpatialProblem):
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x_rng = [0, 5]
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output_variables = ['y']
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spatial_domain = Span({'x': x_rng})
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def ode(input_, output_):
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y = output_
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x = input_
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return grad(y, x) + y - x
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def fixed(input_, output_):
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exp_value = 1.
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return output_ - exp_value
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def solution(self, input_):
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x = input_
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return x - 1.0 + 2*torch.exp(-x)
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conditions = {
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'bc': Condition(Span({'x': x_rng[0]}), fixed),
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'dd': Condition(Span({'x': x_rng}), ode),
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}
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truth_solution = solution
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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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args = parser.parse_args()
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problem = FirstOrderODE()
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model = FeedForward(
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layers=[4]*2,
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output_variables=problem.output_variables,
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input_variables=problem.input_variables,
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func=Softplus,
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)
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pinn = PINN(problem, model, lr=0.03, error_norm='mse', regularizer=0)
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if args.s:
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pinn.span_pts(
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{'variables': ['x'], 'mode': 'grid', 'n': 1}, locations=['bc'])
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pinn.span_pts(
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{'variables': ['x'], 'mode': 'grid', 'n': 30}, locations=['dd'])
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Plotter().plot_samples(pinn, ['x'])
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pinn.train(1200, 50)
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pinn.save_state('pina.ode')
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else:
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pinn.load_state('pina.ode')
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plotter = Plotter()
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plotter.plot(pinn, components=['y'])
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