Learning hydrodynamic equations for active matter from particle simulations and experiments.

ABSTRACT: Recent advances in high-resolution imaging techniques and particle-based simulation methods have enabled the precise microscopic characterization of collective dynamics in various biological and engineered active matter systems. In parallel, data-driven algorithms for learning interpretable continuum models have shown promising potential for the recovery of underlying partial differential equations (PDEs) from continuum simulation data. By contrast, learning macroscopic hydrodynamic equations for active matter directly from experiments or particle simulations remains a major challenge, especially when continuum models are not known a priori or analytic coarse graining fails, as often is the case for nondilute and heterogeneous systems. Here, we present a framework that leverages spectral b

SUBMITTER: Supekar R 

PROVIDER: S-EPMC9963139 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature