Physics-conforming Latent Twins
Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physical systems. For time-dependent problems, however, accurate interpolation of training trajectories is not sufficient: reliable surrogates should also respect the conservation laws, invariants, admissibility conditions, and dissipative structures that give those trajectories physical meaning. We introduce Physics-conforming Latent Twins, a framework for learning latent surrogate solution operators whose dynamics satisfy selected physical principles by design. The method builds on the Latent Twin formulation by jointly learning an encoder, a dec...
| Authors | Matthias Chung, Yutong Bu, Deepanshu Verma |
|---|---|
| Primary category | cs.LG |
| Published | 13 June 2026 |
| Last updated at source | 24 June 2026 |
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