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arXiv 2201.08623cond-mat.softcs.LGphysics.data-an
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Learning deterministic hydrodynamic equations from stochastic active particle dynamics

Technische Universität Dresden(德累斯顿工业大学);Max Planck Institute of Molecular Cell Biology and Genetics(马克斯·普朗克分子细胞生物学与遗传学研究所);Center for Systems Biology Dresden(德累斯顿系统生物学中心);Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI(可扩展数据分析与人工智能中心 ScaDS.AI);Cluster of Excellence Physics of Life, TU Dresden(德累斯顿工业大学生命物理卓越集群);Max Planck Institute for the Physics of Complex Systems(马克斯·普朗克复杂系统物理研究所);University of Geneva(日内瓦大学)

Suryanarayana Maddu, Quentin Vagne, Ivo F. Sbalzarini

英文摘要:

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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