Abstract:In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional parameter space $(\eta,\rho,v_0)$. We construct a dataset of simulated parameter points and characterize each point using long-time dynamical observables. These observables are then used as inputs to a K-Means clustering procedure, which assigns each point to a disorder, order, or coexistence phase. Using these clustered labels, we train a neural-network classifier to learn the mapping from model parameters to phase behavior, achieving a classification accuracy of 0.92. The resulting phase map resolves a narrow coexistence region separating the ordered and disordered phases and extends the inferred phase boundaries beyond the originally sampled simulation points. More broadly, this approach provides a systematic way to convert sparse simulation data into a global phase diagram for collective-motion models.
| Comments: | 8 pages, 3 figures |
| Subjects: | Soft Condensed Matter (cond-mat.soft); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.28167 [cond-mat.soft] |
| (or arXiv:2604.28167v1 [cond-mat.soft] for this version) | |
| https://doi.org/10.48550/arXiv.2604.28167 arXiv-issued DOI via DataCite (pending registration) |
From: Brandon Le [view email]
[v1]
Thu, 30 Apr 2026 17:52:23 UTC (2,521 KB)
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