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Updated: Mar 21, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
Estimation of spatial and time scales of collective behaviors of active matter through learning hydrodynamic
Bappaditya Roy1,2, Natsuhiko Yoshinaga1,2,3
1Future University Hakodate, Department of Complex and Intelligent Systems, Kamedanakano-cho 116-2, Hakodate 041-8655, Japan.
Abstract:
We present a data-driven framework for learning hydrodynamic equations from particle-based simulations of active matter. Our method leverages coarse graining in both space and time to bridge microscopic particle dynamics with macroscopic continuum models. By employing spectral representations and sparse regression, we efficiently estimate partial differential equations that capture collective behaviors such as flocking and phase separation. This approach, validated using hydrodynamic descriptions of the Vicsek model and active Brownian particles, demonstrates the potential of data-driven strategies to uncover the universal features of collective dynamics in active matter systems.
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