Details

Autor(en) / Beteiligte

• Ehre, Max
• Papaioannou, Iason
• Willcox, Karen E.
• Straub, Daniel

Titel

Conditional reliability analysis in high dimensions based on controlled mixture importance sampling and information reuse

Ist Teil von

• Computer methods in applied mechanics and engineering, 2021-08, Vol.381 (C), p.113826, Article 113826

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In many contexts, it is of interest to assess the impact of selected parameters on the failure probability of a physical system. To this end, one can perform conditional reliability analysis, in which the probability of failure becomes a function of these parameters. Computing conditional reliability requires recomputing failure probabilities for a sample sequence of the parameters, which strongly increases the already high computational cost of conventional reliability analysis. We alleviate these costs by reusing information from previous reliability computations in each subsequent reliability analysis of the sequence. The method is designed using two variants of importance sampling and performs information transfer by reusing importance densities from previous reliability analyses in the current one. We put forward a criterion for selecting the most informative importance densities, which is robust with respect to the input space dimension, and use a recently proposed density mixture model for constructing effective importance densities in high dimensions. The method controls the estimator coefficient of variation to achieve a prescribed accuracy. We demonstrate its performance by means of two engineering examples featuring a number of pitfall features such as strong non-linearity, high dimensionality and small failure probabilities (10−5to10−9). •Conditional RA yields the probability of failure in function of the system inputs.•This is useful in reliability-oriented UQ and reliability-based decision making.•Conditional RA requires solving a series of RA problems and is therefore expensive.•We present an approach using information reuse and controlled importance sampling.•The efficacy of our method and benefits of conditional RA are showcased in examples.