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arXiv Machine Learning By Sofianos Panagiotis Fotias, Vassilis Gaganis

Inducing Permutation Invariant Priors in Bayesian Optimization for Carbon Capture and Storage Applications

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The paper introduces a new Gaussian Process kernel, GP‑Perm, that incorporates permutation invariance for Bayesian Optimization tasks involving well placement in Carbon Capture and Storage (CCS) projects. It compares sets via a stable divergence between their empirical representations and can be combined with standard kernels for additional inputs. The authors also explore a Deep Kernel Learning model using a Deep Sets architecture as a learned invariant baseline, evaluating both approaches on eight use cases, including seven synthetic benchmarks and a realistic CCS case study in the Johansen formation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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