pymordemos.data_driven

Module Contents

pymordemos.data_driven.create_fom(fv, grid_intervals)

pymordemos.data_driven.main(regressor: Choices('fcnn vkoga gpr') = Argument(..., help="Regressor to use. Options are neural networks using PyTorch, pyMOR's VKOGA algorithm or Gaussian process regression using scikit-learn."), grid_intervals: int = Argument(..., help='Grid interval count.'), training_samples: int = Argument(..., help='Number of samples used for computing the reduced basis and training the regressor.'), fv: bool = Option(False, help='Use finite volume discretization instead of finite elements.'), vis: bool = Option(False, help='Visualize full order solution and reduced solution for a test set.'), validation_ratio: float = Option(0.1, help='Ratio of training data used for validation of the neural networks.'), input_scaling: bool = Option(False, help='Scale the input of the regressor (i.e. the parameter).'), output_scaling: bool = Option(False, help='Scale the output of the regressor (i.e. reduced coefficients or output quantity.'))

Model order reduction with machine learning methods (approach by Hesthaven and Ubbiali).