Abstract Model calibration is crucial for optimizing the performance of complex computer
models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological …

We develop a new computational framework to solve sequential Bayesian optimal
experimental design (SBOED) problems constrained by large-scale partial differential …

We perform Bayesian model calibration of two-nucleon ($ NN $) low-energy constants
(LECs) appearing in an $ NN $ interaction based on pionless effective field theory (EFT) …

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of
the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator …

Simulation models often have parameters as input and return outputs to understand the
behavior of complex systems. Calibration is the process of estimating the values of the …

Almost all science and engineering applications eventually stop scaling: their runtime no
longer decreases as available computational resources increase. Therefore, many …

This white paper is the result of a collaboration by those that attended a workshop at the
Facility for Rare Isotope Beams (FRIB), organized by the FRIB Theory Alliance (FRIB-TA), on …

Abstract The Kennedy and O'Hagan (KOH) calibration framework uses coupled Gaussian
processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs”(calibration …

Digital twins have been actively explored in many engineering applications, such as
manufacturing and autonomous systems. However, model discrepancy is ubiquitous in most …

A Bayesian optimization procedure is presented for calibrating a multimechanism
micromechanical model for creep to experimental data of F82H steel. Reduced activation …