Orthogonal Discrepancy Kernels for Learning with Partial Physics

Swapnil Manna; Timothy J. Rogers; Lawrence Bull

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arXiv · 2606.21199

Orthogonal Discrepancy Kernels for Learning with Partial Physics

Swapnil Manna·Timothy J. Rogers·Lawrence Bull

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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.

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Swapnil Manna, Timothy J. Rogers, Lawrence Bull. 2026-07-02. Orthogonal Discrepancy Kernels for Learning with Partial Physics. https://arxiv.org/abs/2606.21199

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