Sequential Bayesian experimental design for calibration of expensive simulation models

Abstract

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.

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Cited by in corpus (5)

• Sequential Bayesian experimental design for calibration of expensive simulation models
• Active Learning of Model Discrepancy with Bayesian Experimental Design
• Simulation Experiment Design for Calibration via Active Learning
• Portable, heterogeneous ensemble workflows at scale using libEnsemble
• Augmenting a simulation campaign for hybrid computer model and field data experiments