{"ID":2858489,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.08865","arxiv_id":"2510.08865","title":"Multi-fidelity Batch Active Learning for Gaussian Process Classifiers","abstract":"Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study efficient allocation of a simulation budget using a Gaussian Process (GP) model in the binary simulation output case. This paper introduces Bernoulli Parameter Mutual Information (BPMI), a batch active learning algorithm for multi-fidelity GP classifiers. BPMI circumvents the intractability of calculating mutual information in the probability space by employing a first-order Taylor expansion of the link function. We evaluate BPMI against several baselines on two synthetic test cases and a complex, real-world application involving the simulation of a laser-ignited rocket combustor. In all experiments, BPMI demonstrates superior performance, achieving higher predictive accuracy for a fixed computational budget.","short_abstract":"Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study efficient allocation of a simulation budget using a Gaussian Process (GP) model in the binary simulation output case. This paper introduces Ber...","url_abs":"https://arxiv.org/abs/2510.08865","url_pdf":"https://arxiv.org/pdf/2510.08865v1","authors":"[\"Murray Cutforth\",\"Yiming Yang\",\"Tiffany Fan\",\"Serge Guillas\",\"Eric Darve\"]","published":"2025-10-09T23:45:38Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CE\",\"physics.comp-ph\"]","methods":"[\"LoRA\"]","has_code":false}