Model orthogonalization and Bayesian forecast mixing via principal component analysis

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

doi:10.1103/PhysRevResearch.6.033266

Title: Model orthogonalization and Bayesian forecast mixing via principal component analysis

Journal Article · 09 September 2024 · Physical Review Research

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

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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. Published by the American Physical Society 2024

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Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP)

Grant/Contract Number:: SC0023688

OSTI ID:: 2440073

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

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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