March 13, 2024, 4:44 a.m. | Francesco Romor, Marco Tezzele, Gianluigi Rozza

stat.ML updates on

arXiv:2107.10867v3 Announce Type: replace
Abstract: Parameter space reduction has been proved to be a crucial tool to speed-up the execution of many numerical tasks such as optimization, inverse problems, sensitivity analysis, and surrogate models' design, especially when in presence of high-dimensional parametrized systems. In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to carry out a more efficient dimension reduction in the parameter …

abstract analysis arxiv classification design numerical optimization regression sensitivity space speed systems tasks tool type work

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