thermalblock_adaptive

pymor-demo thermalblock_adaptive [OPTIONS] RBSIZE

Modified thermalblock demo using adaptive greedy basis generation algorithm.

Arguments:

RBSIZE

Size of the reduced basis. [Required]

Parameters:

--cache-region

Name of cache region to use for caching solution snapshots. [Choices: none, memory, disk, persistent, Default: none]

--error-estimator, --no-error-estimator

Use error estimator for basis generation. [Default: True]

--gamma

Weight factor for age penalty term in refinement indicators. [Default: 0.2]

--grid

Use grid with 2*NI*NI elements. [Default: 100]

--ipython-engines

If positive, the number of IPython cluster engines to use for parallel greedy search. If zero, no parallelization is performed. [Default: 0]

--ipython-profile

IPython profile to use for parallelization.

--list-vector-array, --no-list-vector-array

Solve using ListVectorArray[NumpyVector] instead of NumpyVectorArray. [Default: False]

--pickle

Pickle reduced discretization, as well as reductor and high-dimensional model to files with this prefix.

--plot-err, --no-plot-err

Plot error. [Default: False]

--plot-solutions, --no-plot-solutions

Plot some example solutions. [Default: False]

--plot-error-sequence, --no-plot-error-sequence

Plot reduction error vs. basis size. [Default: False]

--product

Product w.r.t. which to orthonormalize and calculate Riesz representatives. [Choices: euclidean, h1, Default: h1]

--reductor

Reductor (error estimator) to choose (traditional, residual_basis). [Choices: traditional, residual_basis, Default: residual_basis]

--rho

Maximum allowed ratio between error on validation set and on training set. [Default: 1.1]

--test

Use COUNT snapshots for stochastic error estimation. [Default: 10]

--theta

Ratio of elements to refine. [Default: 0.0]

--validation-mus

Size of validation set. [Default: 0]

--visualize-refinement, --no-visualize-refinement

Visualize the training set refinement indicators. [Default: True]