Reusing Information for Multifidelity Active Learning in Reliability-Based Design Optimization

Abstract

This paper develops a multifidelity method to reuse information from optimization history for adaptively refining surrogates in reliability-based design optimization (RBDO). RBDO can be computationally prohibitive due to numerous evaluations of the expensive high-fidelity models to estimate the probability of failure of the system in each optimization iteration. In this work, the high-fidelity model evaluations are replaced by cheaper-to-evaluate adaptively refined surrogate evaluations in the probability of failure estimation. The method reuses the past optimization iterations as an information source for devising an efficient multifidelity active learning (adaptive sampling) algorithm to refine the surrogates that best locate the failure boundary. We implement the information-reuse method using a multifidelity extension of efficient global reliability analysis that combines the expected feasibility function with a weighted lookahead information gain criterion to pick both the next sample location and information source used to evaluate the sample.