contributor author | Pandita, Piyush | |
contributor author | Bilionis, Ilias | |
contributor author | Panchal, Jitesh | |
date accessioned | 2019-09-18T09:02:41Z | |
date available | 2019-09-18T09:02:41Z | |
date copyright | 7/10/2019 12:00:00 AM | |
date issued | 2019 | |
identifier issn | 1050-0472 | |
identifier other | md_141_10_101404 | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4258204 | |
description abstract | Bayesian optimal design of experiments (BODEs) have been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE methods operate in specific contexts like optimization, or learning a universal representation of the black-box function. The objective of this paper is to design a BODE for estimating the statistical expectation of a physical response surface. This QoI is omnipresent in uncertainty propagation and design under uncertainty problems. Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI. We represent the information gain from a hypothetical experiment as the Kullback–Liebler (KL) divergence between the prior and the posterior probability distributions of the QoI. The prior distribution of the QoI is conditioned on the observed data, and the posterior distribution of the QoI is conditioned on the observed data and a hypothetical experiment. The main contribution of this paper is the derivation of a semi-analytic mathematical formula for the expected information gain about the statistical expectation of a physical response. The developed BODE is validated on synthetic functions with varying number of input-dimensions. We demonstrate the performance of the methodology on a steel wire manufacturing problem. | |
publisher | American Society of Mechanical Engineers (ASME) | |
title | Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions | |
type | Journal Paper | |
journal volume | 141 | |
journal issue | 10 | |
journal title | Journal of Mechanical Design | |
identifier doi | 10.1115/1.4043930 | |
journal fristpage | 101404 | |
journal lastpage | 101404-11 | |
tree | Journal of Mechanical Design:;2019:;volume( 141 ):;issue: 010 | |
contenttype | Fulltext | |