OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems
Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade-off: manually prescribed mechanistic models are interpretable by design but often overly simplistic and misspecified; in contrast, flexible data-driven neural methods lack physical insight. Hybrid modeling aims for the best of both worlds by combining a prescribed or symbolic, physics-based component with a flexible neural network. A critical challenge, however, is that the neural component may relearn mechanistic parts, yielding redundant and uninterpretable models, especially when the symbolic structure itself is discovered from data. Existing methods based on standard $L^2$ re...
| Authors | Till Richter, Niki Kilbertus |
|---|---|
| Primary category | cs.LG |
| Published | 17 June 2026 |
| Last updated at source | 6 July 2026 |
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