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Adaptive Gaussian Process Regression for Bayesian inverse problems

  • We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.
Metadaten
Author:Paolo Villani, Jörg F. Unger, Martin WeiserORCiD
Document Type:In Proceedings
Parent Title (English):Proceedings of the Conference Algoritmy 2024
First Page:214
Last Page:224
Year of first publication:2024
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