Bayesian updating using multi-fidelity active learning Kriging models

doi:10.1016/j.cma.2025.118658

Bayesian updating using multi-fidelity active learning Kriging models

2025-12-24

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OA

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Ioannis Prentzas

M

Michalis Fragiadakis *

DOI：10.1016/j.cma.2025.118658

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• An active learning Kriging methodology for Bayesian updating is presented. • The BUS method recasts the updating problem into an equivalent reliability problem, solved with a Kriging metamodel. • A multi-fidelity approach is employed in the active learning Kriging model, where models of different accuracy are adopted. • Multi-fidelity Bayesian optimization is used for calculating the constant c of the BUS method. • An improved version of the Quantified Active learning Subset Simulation method is proposed to define the posterior samples.

Keywords:

Bayesian updating

Active learning

Kriging

BUS method

Surrogate models

Multi-fidelity models

Bayesian updating using multi-fidelity active learning Kriging models

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