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Orthogonal Discrepancy Kernels for Learning with Partial Physics

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arXiv:2606. 21199v1 Announce Type: new Abstract: We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components.

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Authors: Swapnil Manna, Timothy J. Rogers, Lawrence Bull
Released: 2026-06-23 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Orthogonal Discrepancy Kernels for Learning with Partial Physics overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: line, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.21199

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