Application of Predictive Model Selection to Coupled Models

TL;DR

This paper introduces a Bayesian model selection method for coupled models to improve the prediction of unobserved quantities in critical applications like climate and aerospace, emphasizing robustness of the predictive distribution.

Contribution

It presents a novel predictive Bayesian model selection approach specifically designed for coupled models to enhance prediction accuracy of unobserved quantities.

Findings

The method effectively identifies the best coupled model for prediction.

Robust predictive distributions lead to improved accuracy in QoI predictions.

Application to a model problem demonstrates the approach's effectiveness.

Abstract

A predictive Bayesian model selection approach is presented to discriminate coupled models used to predict an unobserved quantity of interest (QoI). The need for accurate predictions arises in a variety of critical applications such as climate, aerospace and defense. A model problem is introduced to study the prediction yielded by the coupling of two physics/sub-components. For each single physics domain, a set of model classes and a set of sensor observations are available. A goal-oriented algorithm using a predictive approach to Bayesian model selection is then used to select the combination of single physics models that best predict the QoI. It is shown that the best coupled model for prediction is the one that provides the most robust predictive distribution for the QoI.