Model orthogonalization and Bayesian forecast mixing via principal component analysis

Giuliani, P.; Godbey, K.; Kejzlar, V.; Nazarewicz, W.

doi:10.1103/PhysRevResearch.6.033266

Model orthogonalization and Bayesian forecast mixing via principal component analysis

Journal Article · Mon Sep 09 00:00:00 EDT 2024 · Physical Review Research

DOI:https://doi.org/10.1103/PhysRevResearch.6.033266· OSTI ID:2440073

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Michigan State University, East Lansing, MI (United States)
Skidmore College, Saratoga Springs, NY (United States)

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. In this paper we describe a method based on the principal component analysis that eliminates model redundancy. We show that by adding model orthogonalization to the proposed Bayesian model combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.

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Research Organization:: Michigan State University, East Lansing, MI (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Nuclear Physics (NP)

Grant/Contract Number:: SC0013365; SC0023688

OSTI ID:: 2440073

Alternate ID(s):: OSTI ID: 2572468

Journal Information:: Physical Review Research, Journal Name: Physical Review Research Journal Issue: 3 Vol. 6; ISSN 2643-1564

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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