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Orthogonal Discrepancy Kernels for Learning with Partial ...
[Submitted on 19 Jun 2026] · 2026-06-23 · via stat updates on arXiv.org

Statistics > Machine Learning

arXiv:2606.21199 (stat)

[Submitted on 19 Jun 2026]

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Abstract:We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Orthogonal Gaussian process regression balances sparse parameter selection (the white box) with discrepancy learning (the black box) to produce interpretable models from incomplete physics.

Submission history

From: Swapnil Manna [view email]
[v1] Fri, 19 Jun 2026 08:10:18 UTC (409 KB)

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