Model selection for imperfect computer codes under discrepancy-aware calibration: application to IATE closure modelling
Abstract
Selecting among competing computer models is challenging when all candidates are imperfect representations of reality. In discrepancy-aware Bayesian calibration, classical evidence-based criteria (e.g. Bayes factors) can become difficult to interpret. In particular, marginal likelihood integrates over flexible discrepancy components and can exhibit pronounced sensitivity to prior assumptions on discrepancy hyperparameters. In this paper, we revisit model selection under additive discrepancy and propose a posterior-averaged error criterion that quantifies the magnitude of the residual systematic correction required by each model after calibration. We develop the criterion within a modular calibration framework, i.e. the Full Maximum a Posteriori (FMP) approximation, and analyze its empirical robustness to hyperparameter priors on a pedagogical example, and compare it to Bayes factors and information criteria. We then apply the methodology to the modeling of a PDE problem arising in multiphase flow simulations. The objective is to select among alternative closure models for source and sink terms in the two-group Interfacial Area Transport Equation (IATE), using experimental interfacial area measurements across multiple flow regimes in large-diameter vertical air-water pipe flows. Results show that Bayes-factor conclusions can change markedly depending on whether discrepancy is included and on the discrepancy prior, whereas the posterior-averaged error provides a stable and physically interpretable ranking by isolating irreducible model bias.
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BibTeX
@misc{idrissi2026model,
title = {{Model selection for imperfect computer codes under discrepancy-aware calibration: application to IATE closure modelling}},
author = {Sanae Janati Idrissi and Maria Giovanna Rodio and Pietro Marco Congedo},
howpublished = {HAL},
year = {2026},
month = jun,
url = {https://hal.science/hal-05788457v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Model selection for imperfect computer codes under discrepancy-aware calibration: application to IATE closure modelling
AU - Sanae Janati Idrissi
AU - Maria Giovanna Rodio
AU - Pietro Marco Congedo
PY - 2026
DA - 2026/06/20
PB - HAL
UR - https://hal.science/hal-05788457v1
AB - Selecting among competing computer models is challenging when all candidates are imperfect representations of reality. In discrepancy-aware Bayesian calibration, classical evidence-based criteria (e.g. Bayes factors) can become difficult to interpret. In particular, marginal likelihood integrates over flexible discrepancy components and can exhibit pronounced sensitivity to prior assumptions on discrepancy hyperparameters. In this paper, we revisit model selection under additive discrepancy and propose a posterior-averaged error criterion that quantifies the magnitude of the residual systematic correction required by each model after calibration. We develop the criterion within a modular calibration framework, i.e. the Full Maximum a Posteriori (FMP) approximation, and analyze its empirical robustness to hyperparameter priors on a pedagogical example, and compare it to Bayes factors and information criteria. We then apply the methodology to the modeling of a PDE problem arising in multiphase flow simulations. The objective is to select among alternative closure models for source and sink terms in the two-group Interfacial Area Transport Equation (IATE), using experimental interfacial area measurements across multiple flow regimes in large-diameter vertical air-water pipe flows. Results show that Bayes-factor conclusions can change markedly depending on whether discrepancy is included and on the discrepancy prior, whereas the posterior-averaged error provides a stable and physically interpretable ranking by isolating irreducible model bias.
ER -