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examples/run_parametric_elliptic_optimal_control_alpha.py
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207
examples/run_parametric_elliptic_optimal_control_alpha.py
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import numpy as np
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import torch
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import argparse
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from pina.pinn import PINN
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from pina.ppinn import ParametricPINN as pPINN
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from pina.label_tensor import LabelTensor
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from torch.nn import ReLU, Tanh, Softplus
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from pina.adaptive_functions.adaptive_softplus import AdaptiveSoftplus
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from problems.parametric_elliptic_optimal_control_alpha_variable import ParametricEllipticOptimalControl
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from pina.multi_deep_feed_forward import MultiDeepFeedForward
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from pina.deep_feed_forward import DeepFeedForward
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alpha = 1
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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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return (-x[:, 0]**2+1) * (-x[:, 1]**2+1)
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class CustomMultiDFF(MultiDeepFeedForward):
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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['u_param'] * x[:, 3]).reshape(-1, 1), ['p'])
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a = LabelTensor.hstack([out, p])
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return a
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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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# 'u_param': {
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# 'input_variables': ['u', 'mu'],
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# 'output_variables': ['u_param'],
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# 'layers': [],
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# 'func': None
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# },
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# 'p': {
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# 'input_variables': ['u'],
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# 'output_variables': ['p'],
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# 'layers': [10],
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# 'func': None
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# },
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}
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)
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opc = ParametricEllipticOptimalControl(alpha)
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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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# model = DeepFeedForward(
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# layers=[40, 40, 20],
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# output_variables=['u_param', 'y', 'p'],
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# input_variables=opc.input_variables+['mu', 'alpha'],
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# func=Softplus,
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# extra_features=[myFeature()]
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# )
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pinn = pPINN(
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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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lr_accelerate=None)
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if args.s:
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pinn.span_pts(30, 'grid', ['D1'])
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pinn.span_pts(50, 'grid', ['gamma1', 'gamma2', 'gamma3', 'gamma4'])
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pinn.train(10000, 20)
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# with open('ocp_wrong_history.txt', '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).item()))
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pinn.save_state('pina.ocp')
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else:
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pinn.load_state('working.pina.ocp')
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pinn.load_state('pina.ocp')
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import matplotlib
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matplotlib.use('GTK3Agg')
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import matplotlib.pyplot as plt
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# res = 64
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# param = torch.tensor([[3., 1]])
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# pts_container = []
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# for mn, mx in [[-1, 1], [-1, 1]]:
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# pts_container.append(np.linspace(mn, mx, res))
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# grids_container = np.meshgrid(*pts_container)
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# unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T
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# unrolled_pts = torch.cat([unrolled_pts, param.double().repeat(unrolled_pts.shape[0], 1).reshape(-1, 2)], axis=1)
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# unrolled_pts = LabelTensor(unrolled_pts, ['x1', 'x2', 'mu', 'alpha'])
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# Z_pred = pinn.model(unrolled_pts.tensor)
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# print(Z_pred.tensor.shape)
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# plt.subplot(2, 3, 1)
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# plt.pcolor(Z_pred['y'].reshape(res, res).detach())
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# plt.colorbar()
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# plt.subplot(2, 3, 2)
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# plt.pcolor(Z_pred['u_param'].reshape(res, res).detach())
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# plt.colorbar()
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# plt.subplot(2, 3, 3)
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# plt.pcolor(Z_pred['p'].reshape(res, res).detach())
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# plt.colorbar()
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# with open('ocp_mu3_a1_plot.txt', 'w') as f_:
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# f_.write('x y u p ys\n')
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# for (x, y), tru, pre, e in zip(unrolled_pts[:, :2],
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# Z_pred['u_param'].reshape(-1, 1),
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# Z_pred['p'].reshape(-1, 1),
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# Z_pred['y'].reshape(-1, 1),
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# ):
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# f_.write('{} {} {} {} {}\n'.format(x.item(), y.item(), tru.item(), pre.item(), e.item()))
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# param = torch.tensor([[3.0, 0.01]])
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# unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T
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# unrolled_pts = torch.cat([unrolled_pts, param.double().repeat(unrolled_pts.shape[0], 1).reshape(-1, 2)], axis=1)
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# unrolled_pts = LabelTensor(unrolled_pts, ['x1', 'x2', 'mu', 'alpha'])
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# Z_pred = pinn.model(unrolled_pts.tensor)
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# plt.subplot(2, 3, 4)
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# plt.pcolor(Z_pred['y'].reshape(res, res).detach())
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# plt.colorbar()
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# plt.subplot(2, 3, 5)
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# plt.pcolor(Z_pred['u_param'].reshape(res, res).detach())
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# plt.colorbar()
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# plt.subplot(2, 3, 6)
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# plt.pcolor(Z_pred['p'].reshape(res, res).detach())
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# plt.colorbar()
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# plt.show()
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# with open('ocp_mu3_a0.01_plot.txt', 'w') as f_:
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# f_.write('x y u p ys\n')
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# for (x, y), tru, pre, e in zip(unrolled_pts[:, :2],
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# Z_pred['u_param'].reshape(-1, 1),
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# Z_pred['p'].reshape(-1, 1),
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# Z_pred['y'].reshape(-1, 1),
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# ):
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# f_.write('{} {} {} {} {}\n'.format(x.item(), y.item(), tru.item(), pre.item(), e.item()))
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y = {}
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u = {}
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for alpha in [0.01, 0.1, 1]:
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y[alpha] = []
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u[alpha] = []
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for p in np.linspace(0.5, 3, 32):
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a = pinn.model(LabelTensor(torch.tensor([[0, 0, p, alpha]]).double(), ['x1', 'x2', 'mu', 'alpha']).tensor)
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y[alpha].append(a['y'].detach().numpy()[0])
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u[alpha].append(a['u_param'].detach().numpy()[0])
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plt.plot(np.linspace(0.5, 3, 32), u[1], label='u')
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plt.plot(np.linspace(0.5, 3, 32), u[0.01], label='u')
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plt.plot(np.linspace(0.5, 3, 32), u[0.1], label='u')
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plt.plot([1, 2, 3], [0.28, 0.56, 0.85], 'o', label='Truth values')
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plt.legend()
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plt.show()
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print(y[1])
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print(y[0.1])
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print(y[0.01])
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with open('elliptic_param_y.txt', 'w') as f_:
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f_.write('mu 1 01 001\n')
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for mu, y1, y01, y001 in zip(np.linspace(0.5, 3, 32), y[1], y[0.1], y[0.01]):
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f_.write('{} {} {} {}\n'.format(mu, y1, y01, y001))
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with open('elliptic_param_u.txt', 'w') as f_:
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f_.write('mu 1 01 001\n')
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for mu, y1, y01, y001 in zip(np.linspace(0.5, 3, 32), u[1], u[0.1], u[0.01]):
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f_.write('{} {} {} {}\n'.format(mu, y1, y01, y001))
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plt.plot(np.linspace(0.5, 3, 32), y, label='y')
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plt.plot([1, 2, 3], [0.062, 0.12, 0.19], 'o', label='Truth values')
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plt.legend()
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plt.show()
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