Source code for pymordemos.neural_networks_instationary

#!/usr/bin/env python
# This file is part of the pyMOR project (
# Copyright 2013-2020 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (

import numpy as np
from typer import Argument, run

from pymor.basic import *
from pymor.core.config import config
from pymor.core.exceptions import TorchMissing

[docs]def main( grid_intervals: int = Argument(..., help='Grid interval count.'), time_steps: int = Argument(..., help='Number of time steps used for discretization.'), training_samples: int = Argument(..., help='Number of samples used for training the neural network.'), validation_samples: int = Argument(..., help='Number of samples used for validation during the training phase.'), ): """Model oder reduction with neural networks for an instationary problem (approach by Hesthaven and Ubbiali). """ if not config.HAVE_TORCH: raise TorchMissing() fom = create_fom(grid_intervals, time_steps) parameter_space =, 2.) from pymor.reductors.neural_network import NeuralNetworkInstationaryReductor training_set = parameter_space.sample_uniformly(training_samples) validation_set = parameter_space.sample_randomly(validation_samples) reductor = NeuralNetworkInstationaryReductor(fom, training_set, validation_set, basis_size=10) rom = reductor.reduce(hidden_layers='[30, 30, 30]', restarts=100) test_set = parameter_space.sample_randomly(10) speedups = [] import time print(f'Performing test on set of size {len(test_set)} ...') U = fom.solution_space.empty(reserve=len(test_set)) U_red = fom.solution_space.empty(reserve=len(test_set)) for mu in test_set: tic = time.time() U.append(fom.solve(mu)) time_fom = time.time() - tic tic = time.time() U_red.append(reductor.reconstruct(rom.solve(mu))) time_red = time.time() - tic speedups.append(time_fom / time_red) absolute_errors = (U - U_red).l2_norm() relative_errors = (U - U_red).l2_norm() / U.l2_norm() print(f'Average absolute error: {np.average(absolute_errors)}') print(f'Average relative error: {np.average(relative_errors)}') print(f'Median of speedup: {np.median(speedups)}')
[docs]def create_fom(grid_intervals, time_steps): problem = burgers_problem() print('Discretize ...') discretizer = discretize_instationary_fv fom, _ = discretizer(problem, diameter=1. / grid_intervals, nt=time_steps) return fom
if __name__ == '__main__': run(main)