Neuro-Symbolic ODE Discovery with Latent Grammar Flow
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914483815841792 |
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| author | Yu, Karin Chatzi, Eleni Kissas, Georgios |
| author_facet | Yu, Karin Chatzi, Eleni Kissas, Georgios |
| contents | 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, can be either embedded into the rules or used as conditional predictors. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_16232 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neuro-Symbolic ODE Discovery with Latent Grammar Flow Yu, Karin Chatzi, Eleni Kissas, Georgios Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Symbolic Computation 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, can be either embedded into the rules or used as conditional predictors. |
| title | Neuro-Symbolic ODE Discovery with Latent Grammar Flow |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Symbolic Computation |
| url | https://arxiv.org/abs/2604.16232 |