Learning deterministic hydrodynamic equations from stochastic active particle dynamics

• URL: http://arxiv.org/abs/2201.08623v1
• Date: Fri, 21 Jan 2022 10:19:36 GMT
• Title: Learning deterministic hydrodynamic equations from stochastic active particle dynamics
• Authors: Suryanarayana Maddu, Quentin Vagne, Ivo F. Sbalzarini
• Abstract summary: We apply our method to learning a hydrodynamic model for the propagating density lanes observed in self-propelled particle systems. This demonstrates that statistical learning theory combined with physical priors can enable discovery of multi-scale models of non-equilibrium processes.
• Score: 1.933681537640272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories. We apply our method to learning a hydrodynamic model for the propagating density lanes observed in self-propelled particle systems and to learning a continuum description of cell dynamics in epithelial tissues. We also infer from stochastic particle trajectories the latent phoretic fields driving chemotaxis. This demonstrates that statistical learning theory combined with physical priors can enable discovery of multi-scale models of non-equilibrium stochastic processes characteristic of collective movement in living systems.

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