Model order reduction in subset simulations using the proper orthogonal decomposition

نویسندگان

چکیده

Abstract The crude Monte Carlo method is computationally expensive. Hence, incorporating model order reduction methods enabling reliability analysis for high‐dimensional problems necessary. However, this strategy may result in an inaccurate estimation of the probability failure rare events two reasons. First, reduction, represented by proper orthogonal decomposition (POD) here, requires response information form snapshots a priori. To capture essential nonlinear behavior, we propose to update modes using extreme events. Second, simulation many samples estimate low probabilities reliably. end, subset found wide application reduce computational effort. Following strategy, proposed gradually move toward region. Thus, updates particularly promising evaluating from current This contribution shows efficiency POD within simulations. We then leverage updating each subset.

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ژورنال

عنوان ژورنال: Proceedings in applied mathematics & mechanics

سال: 2023

ISSN: ['1617-7061']

DOI: https://doi.org/10.1002/pamm.202300053