CONTOUR LOCATION FOR RELIABILITY IN AIRFOIL SIMULATION EXPERIMENTS USING DEEP GAUSSIAN PROCESSES

• 成果类型: Article
• 署名作者: Booth, Annie s.; Renganathan, S. ashwin; Gramacy, Robert b.
• 署名单位: North Carolina State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Virginia Polytechnic Institute & State University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1951
• 发表日期: 2025
• 页码: 191-211
• 关键词: efficient; DESIGN; optimization; uncertainty; inference

摘要

Bayesian deep Gaussian processes (DGPs) outperform ordinary GPs as surrogate models of complex computer experiments when response surface dynamics are nonstationary, which is especially prevalent in aerospace simulations. Yet DGP surrogates have not been deployed for the canonical downstream task in that setting: reliability analysis through contour location (CL). In that context we are motivated by a simulation of an RAE-2822 transonic airfoil which demarcates efficient and inefficient flight conditions. Level sets separating passable vs. failable operating conditions are best learned through strategic sequential designs. There are two limitations to modern CL methodology which hinder DGP integration in this setting. First, derivative-based optimization underlying acquisition functions is thwarted by sampling-based Bayesian (i.e., MCMC) inference, which is essential for DGP posterior integration. Second, canonical acquisition criteria, such as entropy, are famously myopic to the extent that optimization may even be undesirable. Here we tackle both of these limitations at once, proposing a hybrid criterion that explores along the Pareto front of entropy and (predictive) uncertainty, requiring evaluation only at strategically located triangulation candidates. We showcase DGP CL performance in several synthetic benchmark exercises and on the RAE-2822 airfoil.