{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-optimal-design-of-experiments-for","title":"Bayesian Optimal Design of Experiments For Inferring The Statistical Expectation Of A Black-Box Function","arxiv_id":"1807.09979","date":"2018-07-26","proceeding":null,"authors":["Piyush Pandita","Ilias Bilionis","Jitesh Panchal"],"abstract":"Bayesian optimal design of experiments (BODE) has been successful in\nacquiring information about a quantity of interest (QoI) which depends on a\nblack-box function. BODE is characterized by sequentially querying the function\nat specific designs selected by an infill-sampling criterion. However, most\ncurrent BODE methods operate in specific contexts like optimization, or\nlearning a universal representation of the black-box function. The objective of\nthis paper is to design a BODE for estimating the statistical expectation of a\nphysical response surface. This QoI is omnipresent in uncertainty propagation\nand design under uncertainty problems. Our hypothesis is that an optimal BODE\nshould be maximizing the expected information gain in the QoI. We represent the\ninformation gain from a hypothetical experiment as the Kullback-Liebler (KL)\ndivergence between the prior and the posterior probability distributions of the\nQoI. The prior distribution of the QoI is conditioned on the observed data and\nthe posterior distribution of the QoI is conditioned on the observed data and a\nhypothetical experiment. The main contribution of this paper is the derivation\nof a semi-analytic mathematical formula for the expected information gain about\nthe statistical expectation of a physical response. The developed BODE is\nvalidated on synthetic functions with varying number of input-dimensions. We\ndemonstrate the performance of the methodology on a steel wire manufacturing\nproblem.","url_abs":"http://arxiv.org/abs/1807.09979v3","url_pdf":"http://arxiv.org/pdf/1807.09979v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bayesian-optimal-design-of-experiments-for","repo_url":"https://github.com/piyushpandita92/bode","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}