Module Contents

pymordemos.thermalblock_adaptive.main(rbsize: int = Argument(..., help='Size of the reduced basis.'), cache_region: Choices('none memory disk persistent') = Option('none', help='Name of cache region to use for caching solution snapshots.'), error_estimator: bool = Option(True, help='Use error estimator for basis generation.'), gamma: float = Option(0.2, help='Weight factor for age penalty term in refinement indicators.'), grid: int = Option(100, help='Use grid with 2*NI*NI elements.'), 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.'), list_vector_array: bool = Option(False, help='Solve using ListVectorArray[NumpyVector] instead of NumpyVectorArray.'), pickle: str = Option(None, help='Pickle reduced discretization, as well as reductor and high-dimensional model to files with this prefix.'), plot_err: bool = Option(False, help='Plot error.'), plot_solutions: bool = Option(False, help='Plot some example solutions.'), plot_error_sequence: bool = Option(False, help='Plot reduction error vs. basis size.'), product: Choices('euclidean h1') = Option('h1', help='Product  w.r.t. which to orthonormalize and calculate Riesz representatives.'), reductor: Choices('traditional residual_basis') = Option('residual_basis', help='Reductor (error estimator) to choose (traditional, residual_basis).'), rho: float = Option(1.1, help='Maximum allowed ratio between error on validation set and on training set.'), test: int = Option(10, help='Use COUNT snapshots for stochastic error estimation.'), theta: float = Option(0.0, help='Ratio of elements to refine.'), validation_mus: int = Option(0, help='Size of validation set.'), visualize_refinement: bool = Option(True, help='Visualize the training set refinement indicators.'))[source]

Modified thermalblock demo using adaptive greedy basis generation algorithm.