Response probability distribution estimation of expensive computer simulators: A Bayesian active learning perspective using Gaussian process regression

TL;DR

This paper introduces a Bayesian active learning method using Gaussian process regression to efficiently estimate response probability distributions of expensive computer simulators, addressing accuracy and uncertainty quantification challenges.

Contribution

It presents a novel Bayesian inference framework and active learning strategy for response distribution estimation, incorporating prior knowledge and probabilistic error propagation.

Findings

Efficient distribution estimation demonstrated on five numerical examples.

The method effectively reduces numerical uncertainty to desired levels.

Bayesian active learning improves accuracy with fewer simulator evaluations.

Abstract

Estimation of the response probability distributions of computer simulators in the presence of randomness is a crucial task in many fields. However, achieving this task with guaranteed accuracy remains an open computational challenge, especially for expensive-to-evaluate computer simulators. In this work, a Bayesian active learning perspective is presented to address the challenge, which is based on the use of the Gaussian process (GP) regression. First, estimation of the response probability distributions is conceptually interpreted as a Bayesian inference problem, as opposed to frequentist inference. This interpretation provides several important benefits: (1) it quantifies and propagates discretization error probabilistically; (2) it incorporates prior knowledge of the computer simulator, and (3) it enables the effective reduction of numerical uncertainty in the solution to a prescribed level. The conceptual Bayesian idea is then realized by using the GP regression, where we derive the posterior statistics of the response probability distributions in semi-analytical form and also provide a numerical solution scheme. Based on the practical Bayesian approach, a Bayesian active learning (BAL) method is further proposed for estimating the response probability distributions. In this context, the key contribution lies in the development of two crucial components for active learning, i.e., stopping criterion and learning function, by taking advantage of posterior statistics. It is empirically demonstrated by five numerical examples that the proposed BAL method can efficiently estimate the response probability distributions with desired accuracy.