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Titel:

Solving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Maximilian Dinkel, Carolin M. Geitner, Gil Robalo Rei, Jonas Nitzler, Wolfgang A. Wall
Abstract:
Solving inverse problems using Bayesian methods can become prohibitively expensive when likelihood evaluations involve complex and large scale numerical models. A common approach to circumvent this issue is to approximate the forward model or the likelihood function with a surrogate model. But also there, due to limited computational resources, only a few training points are available in many practically relevant cases. Thus, it can be advantageous to model the additional uncertainties of the su...     »
Stichworte:
Bayesian inverse problem, Gaussian process, active learning
Zeitschriftentitel:
Inverse Problems
Jahr:
2024
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1088/1361-6420/ad5eb4
Status:
Verlagsversion / published
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