Bounds optimization of model response moments

a twin-engine Bayesian active learning method

authored by

Pengfei Wei, Fangqi Hong, Kok-Kwang Phoon, Michael Beer

Abstract

The efficient propagation of imprecise probabilities through expensive simulators has emerged to be one of the great challenges for mixed uncertainty quantification in computational mechanics. An active learning method, named Collaborative and Adaptive Bayesian Optimization (CABO), is developed for tackling this challenge by combining Bayesian Probabilistic Optimization and Bayesian Probabilistic Integration. Two learning functions are introduced as engines for CABO, where one is introduced for realizing the adaptive optimization search in the epistemic uncertainty space, and the other one is developed for adaptive integration in the aleatory uncertainty space. These two engines work in a collaborative way to create optimal design points adaptively in the joint uncertainty space, by which a Gaussian process regression model is trained and updated to approach the bounds of model response moments with pre-specified error tolerances. The effectiveness of CABO is demonstrated using a numerical example and two engineering benchmarks.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

Northwestern Polytechnical University
National University of Singapore
University of Liverpool
Tongji University

Type

Article

Journal

Computational Mechanics

Volume

67

Pages

1273-1292

No. of pages

20

ISSN

0178-7675

Publication date

05.2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Mathematics, Mechanical Engineering, Ocean Engineering, Applied Mathematics, Computational Mechanics, Computational Theory and Mathematics

Electronic version(s)

https://doi.org/10.1007/s00466-021-01977-8 (Access: Closed)