Locally Adaptive Greedy Approximations for Anisotropic Parameter Reduced Basis Spaces
type: Dataset
description: Reduced order models, in particular the reduced basis method, rely on empirically built and problem dependent basis functions that are constructed during an off-line stage. In the on- line stage, the precomputed problem dependent solution space can then be used in order to reduce the size of the computational problem. For complex problems, the number of basis functions required to guarantee a certain error tolerance can become too large in order to benefit computationally from the model reduction. To overcome this, the present work introduces a framework where local approximation spaces (in parameter space) are used to define the reduced order approximation in order to have explicit control over the on-line cost. This approach also adapts the local approximation spaces to local anisotropic behavior in the parameter space. We present the algorithm and present numerous numerical tests.
issued: 2026-05-20T22:20:55.871835
language: en
modified: 2026-05-20T22:20:55.871842
contact point: Organization
  fn: CCSD
keyword: 65c20-65n15-65n30
keyword: greedy-algorithm
keyword: infoeu-reposemanticsarticle
keyword: journal-articles
keyword: mathmath-namathematics-mathnumerical-analysis-mathna
keyword: model-reduction
keyword: reduced-basis-method
landing page: 1064-8275

Locally Adaptive Greedy Approximations for Anisotropic Parameter Reduced Basis Spaces
type: Distribution
format: HTML
issued: 2026-05-20T22:20:55.992842
modified: 2026-05-20T22:20:55.812669
access URL: https://hal.science/hal-00690830

test_moissonnage_selune
type: Agent