most citedAdaptive Gaussian Process Regression for Bayesian inverse problems

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Adaptive Gaussian Process Regression for Bayesian inverse problems

Paolo Villani, Jörg Unger, Martin Weiser

We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward mode…

math.NA2024

Adaptive Gradient Enhanced Gaussian Process Surrogates for Inverse Problems

Phillip Semler, Martin Weiser

Generating simulated training data needed for constructing sufficiently accurate surrogate models to be used for efficient optimization or parameter identification can incur a huge…