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Machine learning maps Vicsek model phase diagram with 92% accuracy

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 3 sources

Researchers have employed machine learning techniques to map the phase diagram of the Vicsek flocking model. By analyzing simulated data and using K-Means clustering, they classified points into disorder, order, or coexistence phases. A neural network was trained on these classifications, achieving 0.92 accuracy in predicting phase behavior and extending the known phase boundaries. AI

Summary written by gemini-2.5-flash-lite from 3 sources. How we write summaries →

IMPACT Demonstrates a systematic method for converting sparse simulation data into global phase diagrams for collective-motion models.

RANK_REASON Academic paper detailing the application of machine learning to a physics model.

Read on arXiv cs.LG →

COVERAGE [3]

  1. arXiv cs.LG TIER_1 · Grace T. Bai, Brandon B. Le ·

    Mapping the Phase Diagram of the Vicsek Model with Machine Learning

    arXiv:2604.28167v1 Announce Type: cross 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 poin…

  2. arXiv cs.LG TIER_1 · Brandon B. Le ·

    Mapping the Phase Diagram of the Vicsek Model with Machine Learning

    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 $(η,ρ,v_0)$. We construct a dataset of simulated parameter points and characterize each point using long-time dynamical…

  3. Hugging Face Daily Papers TIER_1 ·

    Mapping the Phase Diagram of the Vicsek Model with Machine Learning

    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 $(η,ρ,v_0)$. We construct a dataset of simulated parameter points and characterize each point using long-time dynamical…

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