New article: A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences
New article: Schenk, C. and Romero, I. (2026). A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences, Archives of Computational Methods in Engineering. (link).
In this review article, we present a unified framework for the Bayesian calibration of computational models, with particular emphasis on applications involving computationally expensive simulations and scarce experimental data. The article describes four calibration strategies of increasing complexity — covering simple and expensive models, with and without model discrepancy — and introduces ACBICI, a new open-source Python library that implements all of them. The library supports single- and multi-output calibration with Gaussian process surrogates, MCMC and variational inference, and provides practical guidelines for reliable Bayesian calibration in engineering and applied sciences.