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20232026
most citedSolving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning

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cs.CE2026

Efficient Bayesian Optimal Experimental Design for Expensive Computational Models over Finite Design Sets

Maximilian Dinkel, Dragos C. Ana, Benedikt Goderbauer +1

Bayesian calibration is a powerful framework for identifying parameters in complex and large-scale computational models. However, when there is insufficient or poorly suited data f…