Interpretable Physics Extraction from Data for Linear Dynamical Systems using Lie Generator Networks
When the system is linear, why should learning be nonlinear? Linear dynamical systems, the analytical backbone of control theory, signal processing and circuit analysis, have exact closed-form solutions via the state transition matrix. Yet when system parameters must be inferred from data, recent neural approaches offer flexibility at the cost of physical guarantees: Neural ODEs provide flexible trajectory approximation but may violate physical invariants, while energy preserving architectures do not natively represent dissipation essential to real-world systems. We introduce Lie Generator Networks (LGN), which learn a structured generator A and compute trajectories directly via matrix ex...
| Authors | Shafayeth Jamil, Rehan Kapadia |
|---|---|
| Primary category | cs.LG |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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