Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive controllers, schedulers, and observers are often not closed-form and must re-solve a numerical optimization per instance, with the operator re-supplied each time. Physics-informed learning moves this cost to training, but uses a single encoder pathway whose latent geometry de-learns under fine-tuning and admits no quantitative transfer guarantee. We propose an asymmetric two-pathway architecture that resolves both issues. A teacher encoder consumes privileged dense states from a high-fidelity simulator and represents the system through op...
| Authors | Tong Wu, Andrew Campbell, Anna Scaglione |
|---|---|
| Primary category | cs.LG |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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