FoundPDE: A generative foundation model for data-driven symbolic discovery of partial differential equations
Discovering governing partial differential equations (PDEs) from observed data is a central inverse problem in computational science and engineering: the goal is not only to predict trajectories, but to identify interpretable symbolic laws that can be analyzed and reused. Existing PDE discovery methods often depend on hand-designed libraries, accurate numerical derivatives, or per-equation symbolic search, making them fragile under sparse observations, noisy data, broad equation families, and unseen symbolic compositions. Here, we introduce FoundPDE, a pretrained data-to-symbol foundation model for PDE discovery. FoundPDE maps discretized spatio-temporal trajectories to symbolic equations...
| Authors | Langchen Liu, Junlin Luo, Min Zhu et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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