Abstract:Deep generative models such as flow matching and diffusion models have shown great potential in learning complex distributions and dynamical systems, but often act as black-boxes, neglecting underlying physics. In contrast, physics-based simulation models described by ODEs/PDEs remain interpretable, but may have missing or unknown terms, unable to fully describe real-world observations. We bridge this gap with a novel grey-box method that integrates incomplete physics models directly into generative models. Our approach learns dynamics from observational trajectories alone, without ground-truth physics parameters, in a simulation-free manner that avoids scalability and stability issues of Neural ODEs. The core of our method lies in modelling a structured variational distribution within the flow matching framework, by using two latent encodings: one to model the missing stochasticity and multi-modal velocity, and a second to encode physics parameters as a latent variable with a physics-informed prior. Furthermore, we present an adaptation of the framework to handle second-order dynamics. Our experiments on representative ODE/PDE problems and real-world weather forecasting demonstrate that our method performs on par with or superior to fully data-driven approaches and previous grey-box baselines, while preserving the interpretability of the physics model. Our code is available at this https URL.
| Comments: | AISTATS 2026. Code is available at this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.17477 [cs.LG] |
| (or arXiv:2602.17477v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.17477 arXiv-issued DOI via DataCite |
From: Gurjeet Sangra Singh [view email]
[v1]
Thu, 19 Feb 2026 15:43:22 UTC (12,961 KB)
[v2]
Tue, 31 Mar 2026 19:25:14 UTC (12,961 KB)
[v3]
Sat, 25 Apr 2026 14:15:12 UTC (5,665 KB)
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