Actively learning deep Gaussian process models for failure contour and probability estimation

Annie S. Booth, Robert Gramacy, S. Ashwin Renganathan

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

1 Scopus citations

Abstract

Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Queries of computer simulation experiments are computationally expensive, and failures are typically observed in the tails of probability distributions over the input variables, which renders typical Monte Carlo estimation prohibitively expensive. Instead, a statistical "surrogate" model can be leveraged to identify the failure contour from limited simulation data which then informs a biasing distribution for importance-sampling based estimation of failure probabilities. The goal of this work is to explore the viability of active learning for deep Gaussian process (DGP) surrogates towards failure contour and probability estimation problems with expensive simulation models. DGPs outperform traditional GPs in non-stationary settings. Contour locating sequential designs outperform space-filling counterparts. Combined, these result in more accurate failure probability estimates for fixed simulation effort. We demonstrate our method on synthetic test functions as well as two application problems, namely, the RAE-2822 airfoil aerodynamic performance and the thermal stress analysis of a gas turbine blade. We observe that, despite an additional cost for model inference, DGPs offer superior performance in predicting failure probabilities compared to GPs.

Original languageEnglish (US)
Title of host publicationAIAA SciTech Forum and Exposition, 2024
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624107115
DOIs
StatePublished - 2024
EventAIAA SciTech Forum and Exposition, 2024 - Orlando, United States
Duration: Jan 8 2024Jan 12 2024

Publication series

NameAIAA SciTech Forum and Exposition, 2024

Conference

ConferenceAIAA SciTech Forum and Exposition, 2024
Country/TerritoryUnited States
CityOrlando
Period1/8/241/12/24

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

Fingerprint

Dive into the research topics of 'Actively learning deep Gaussian process models for failure contour and probability estimation'. Together they form a unique fingerprint.

Cite this