Multifidelity Gaussian processes for failure boundary and probability estimation

S. Ashwin Renganathan, Vishwas Rao, Ionel M. Navon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation (FPE) involves resolving tails of probability distribution and Monte Carlo (MC) sampling methods are intractable when expensive high-fidelity simulations have to be queried. We propose a method to use models of multiple fidelities, which trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity and thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework, which can automatically select samples (or batches of samples for parallel evaluation) from appropriate fidelity models to make predictions about quantities of interest in the highest fidelity. We use our proposed approach within a importance sampling setting, and demonstrate our method on the failure level set estimation and FPE on synthetic test functions as well as the reliability analysis of a gas turbine engine blade. We demonstrate that our method predicts the failure boundary and probability more accurately and computationally efficiently while using varying fidelity models compared to using just a single fidelity expensive model.

Original languageEnglish (US)
Title of host publicationAIAA SciTech Forum 2022
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624106316
DOIs
StatePublished - 2022
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 - San Diego, United States
Duration: Jan 3 2022Jan 7 2022

Publication series

NameAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
Country/TerritoryUnited States
CitySan Diego
Period1/3/221/7/22

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

Fingerprint

Dive into the research topics of 'Multifidelity Gaussian processes for failure boundary and probability estimation'. Together they form a unique fingerprint.

Cite this