Model Calibration via Distributionally Robust Optimization: On the NASA Langley Uncertainty Quantification Challenge

Autor:	Bai, Yuanlu, Huang, Zhiyuan, Lam, Henry
Rok vydání:	2021
Předmět:	Statistics - Methodology Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
DOI:	10.1016/j.ymssp.2021.108211
Popis:	We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge, a model calibration problem under both aleatory and epistemic uncertainties. Our methodology is based on an integration of robust optimization, more specifically a recent line of research known as distributionally robust optimization, and importance sampling in Monte Carlo simulation. The main computation machinery in this integrated methodology amounts to solving sampled linear programs. We present theoretical statistical guarantees of our approach via connections to nonparametric hypothesis testing, and numerical performances including parameter calibration and downstream decision and risk evaluation tasks. Comment: arXiv admin note: substantial text overlap with arXiv:2006.15689
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.01840 Zobrazit plný text záznamu View this record from Arxiv