Module Contents

pymordemos.burgers_ei.main(exp_min: float = Argument(..., help='Minimal exponent'), exp_max: float = Argument(..., help='Maximal exponent'), ei_snapshots: int = Argument(..., help='Number of snapshots for empirical interpolation.'), ei_size: int = Argument(..., help='Number of interpolation DOFs.'), snapshots: int = Argument(..., help='Number of snapshots for basis generation.'), rb_size: int = Argument(..., help='Size of the reduced basis.'), cache_region: Choices('none memory disk persistent') = Option('disk', help='Name of cache region to use for caching solution snapshots.'), ei_alg: Choices('ei_greedy deim') = Option('ei_greedy', help='Interpolation algorithm to use.'), grid: int = Option(60, help='Use grid with (2*NI)*NI elements.'), grid_type: Choices('rect tria') = Option('rect', help='Type of grid to use.'), initial_data: Choices('sin bump') = Option('sin', help='Select the initial data (sin, bump).'), ipython_engines: int = Option(0, help='If positive, the number of IPython cluster engines to use for parallel greedy search. If zero, no parallelization is performed.'), ipython_profile: str = Option(None, help='IPython profile to use for parallelization.'), lxf_lambda: float = Option(1.0, help='Parameter lambda in Lax-Friedrichs flux.'), periodic: bool = Option(True, help='If not, solve with dirichlet boundary conditions on left and bottom boundary.'), nt: int = Option(100, help='Number of time steps.'), num_flux: Choices('lax_friedrichs engquist_osher') = Option('engquist_osher', help='Numerical flux to use.'), plot_err: bool = Option(False, help='Plot error.'), plot_ei_err: bool = Option(False, help='Plot empirical interpolation error.'), plot_error_landscape: bool = Option(False, help='Calculate and show plot of reduction error vs. basis sizes.'), plot_error_landscape_M: int = Option(10, help='Number of collateral basis sizes to test.'), plot_error_landscape_N: int = Option(10, help='Number of basis sizes to test.'), plot_solutions: bool = Option(False, help='Plot some example solutions.'), test: int = Option(10, help='Number of snapshots to use for stochastic error estimation.'), vx: float = Option(1.0, help='Speed in x-direction.'), vy: float = Option(1.0, help='Speed in y-direction.'))[source]

Reduction of a two-dimensional Burgers-type equation using empirical interpolation.