Data Driven Equation Discovery for Phase-Ordering Dynamics : From Allen Cahn to the Ising Model
Abstract
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 coefficient values are substantially more sensitive to these factors. We further show that enlarging the candidate library can strongly affect both term identification and coefficient recovery. Incorporating library bagging with stability selection reduces this sensitivity and improves the efficiency of equation discovery. For the Glauber spin flip Ising model dynamics, the resulting coarse-grained equation reproduces the characteristic phase-separation and coarsening dynamics of the underlying microscopic system. Overall, our results demonstrate the potential of PDE-SINDy for phase-ordering systems while highlighting the importance of carefully assessing the factors that influence the efficiency of equation discovery.
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BibTeX
@misc{mondal2026data,
title = {{Data Driven Equation Discovery for Phase-Ordering Dynamics : From Allen Cahn to the Ising Model}},
author = {Partha Sarathi Mondal and Manav Kumar Jalan and Anish Kumar and Shradha Mishra},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.20404},
eprint = {2608.20404},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.20404},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Data Driven Equation Discovery for Phase-Ordering Dynamics : From Allen Cahn to the Ising Model
AU - Partha Sarathi Mondal
AU - Manav Kumar Jalan
AU - Anish Kumar
AU - Shradha Mishra
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2608.20404
UR - https://arxiv.org/abs/2608.20404
AB - 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 coefficient values are substantially more sensitive to these factors. We further show that enlarging the candidate library can strongly affect both term identification and coefficient recovery. Incorporating library bagging with stability selection reduces this sensitivity and improves the efficiency of equation discovery. For the Glauber spin flip Ising model dynamics, the resulting coarse-grained equation reproduces the characteristic phase-separation and coarsening dynamics of the underlying microscopic system. Overall, our results demonstrate the potential of PDE-SINDy for phase-ordering systems while highlighting the importance of carefully assessing the factors that influence the efficiency of equation discovery.
ER -