hierarchy

pymor-demo hierarchy [OPTIONS] PROBLEM_NUMBER REGRESSOR GRID_INTERVALS NUM_PARAMETERS

Adaptive model hierarchy combining reduced basis and machine learning methods.

Problem number 0 considers an elliptic problem and problem number 1 considers a parabolic problem.

Arguments:

PROBLEM_NUMBER

Selects the problem to solve [0 or 1]. [Required, Choices: 0, 1]

REGRESSOR

Regressor to use. Options are neural networks using PyTorch, pyMOR’s VKOGA algorithm or Gaussian process regression using scikit-learn. [Required, Choices: fcnn, vkoga, gpr]

GRID_INTERVALS

Grid interval count. [Required]

NUM_PARAMETERS

Number of parameters to evaluate the hierarchy for. [Required]

Parameters:

--time-steps

Number of time steps used for discretization (only used if problem_number is 1). [Default: 10]

--time-vectorized, --no-time-vectorized

Predict the whole time trajectory at once or iteratively. [Default: True]

--vis, --no-vis

Visualize estimated errors for the queried parameters. [Default: False]

--validation-ratio

Ratio of training data used for validation of the neural networks. [Default: 0.1]

--input-scaling, --no-input-scaling

Scale the input of the regressor (i.e. the parameter). [Default: False]

--output-scaling, --no-output-scaling

Scale the output of the regressor (i.e. reduced coefficients or output quantity). [Default: False]