Prediction and Computer Model Calibration Using Outputs From Multi-fidelity Simulators

TL;DR

This paper develops a Bayesian framework that combines multi-fidelity computer simulations and field data to accurately predict physical processes, enabling sensitivity analysis and inverse problem solving.

Contribution

It introduces a novel Bayesian method for integrating multi-fidelity simulators with observational data for improved physical process modeling.

Findings

Effective integration of multi-fidelity models with observations

Enhanced predictive accuracy demonstrated in real application

Framework supports sensitivity analysis and inverse problems

Abstract

Computer codes are widely used to describe physical processes in lieu of physical observations. In some cases, more than one computer simulator, each with different degrees of fidelity, can be used to explore the physical system. In this work, we combine field observations and model runs from deterministic multi-fidelity computer simulators to build a predictive model for the real process. The resulting model can be used to perform sensitivity analysis for the system, solve inverse problems and make predictions. Our approach is Bayesian and will be illustrated through a simple example, as well as a real application in predictive science at the Center for Radiative Shock Hydrodynamics at the University of Michigan.