Module Contents

pymordemos.neural_networks_instationary.create_fom(problem_number, grid_intervals, time_steps)[source]
pymordemos.neural_networks_instationary.discretize_navier_stokes(n, nt)[source]
pymordemos.neural_networks_instationary.main(problem_number: int = Argument(..., min=0, max=1, help='Selects the problem to solve [0 or 1].'), 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.'), plot_test_solutions: bool = Option(False, help='Plot FOM and ROM solutions in the test set.'))[source]

Model oder reduction with neural networks for instationary problems.

Problem number 0 considers the incompressible Navier-Stokes equations in a two-dimensional cavity with the Reynolds number as parameter. The discretization is based on FEniCS.

Problem number 1 considers a parametrized Burgers equation on a one-dimensional domain. The discretization is based on pyMOR’s built-in functionality.