Data Driven Equation Discovery for Phase-Ordering Dynamics : From Allen Cahn to the Ising Model
Data-driven discovery of governing equations from spatiotemporal data offers a promising route to obtaining coarse-grained descriptions of complex dynamical systems. Here, we investigate the performance of PDE-SINDy for discovering phase-ordering dynamics using the Allen--Cahn equation as a benchmark and the Ising model with Glauber spin-flip dynamics as a microscopic system. We systematically analyze the effects of data availability, size of the candidate library, and noise on the efficiency of the equation discovery. We find that stability-selection PDE-SINDy can robustly identify the relevant terms in the governing dynamics even under limited or noisy data, while the recovered coeffici...
| Authors | Partha Sarathi Mondal, Manav Kumar Jalan, Anish Kumar et al. |
|---|---|
| Primary category | cond-mat.soft |
| Published | 4 August 2026 |
| Last updated at source | 3 September 2026 |
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