Neuro-Symbolic ODE Discovery with Latent Grammar Flow
Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and transferability beyond black-box models. We introduce Latent Grammar Flow (LGF), a neuro-symbolic generative framework for discovering ordinary differential equations from data. LGF embeds equations as grammar-based representations into a discrete latent space and forces semantically similar equations to be positioned closer together with a behavioural loss. Then, a discrete flow model guides the sampling process to recursively generate candidate equations that best fit the observed data. Domain knowledge and constraints, such as stability,...
| Authors | Karin Yu, Eleni Chatzi, Georgios Kissas |
|---|---|
| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 13 July 2026 |
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