more scalable isotropic sparse grid #364
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DavidPCoster
approved these changes
Feb 4, 2022
djgroen
approved these changes
Jun 27, 2022
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Made the isotropic sparse grid much more scalable to higher dimensions by making the
compute_sparse_multi_idxsubroutine in the SC sampler more efficient. This used to crash when applying more than 20 inputs. Now it uses the same subroutines as the anisotropic (dimension-adaptive) SC sampler.The
next_level_sparse_gridalso uses dimension-adaptive subroutines, and it is now basically reduced to one line.EDIT: I also modified the PCE-based stats and Sobol subroutines inside the SC analysis class. Turns out you can write a dimension-adaptive SC expansion in the form of a standard PCE expansion, which simplifies the computation of the mean and Sobol indices.
NOTE: the testing always fails at the same point, when getting to
tests/test_pce_analysis_results.py. This does not generate an error though when I test locally.