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Data-Driven Discovery of Active Nematic Hydrodynamics

Chaitanya Joshi, Sattvic Ray, Linnea Lemma, Minu Varghese, Graham J. Sharp, Zvonimir Dogic, Aparna Baskaran, Michael F. Hagan

2022Physical Review Letters35 citationsDOIOpen Access PDF

Abstract

Active nematics can be modeled using phenomenological continuum theories that account for the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistical description of the experiments, the relevant terms in the PDEs and their parameters are usually identified indirectly. We adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from spatiotemporal data, via sparse regression of the coarse-grained fields onto generic low order PDEs. After extensive benchmarking, we apply the method to experiments with microtubule-based active nematics, finding a surprisingly minimal description of the system. Our approach can be generalized to gain insights into active gels, microswimmers, and diverse other experimental active matter systems.

Topics & Concepts

Active matterLiquid crystalPhysicsStatistical physicsPartial differential equationComputer scienceClassical mechanicsOpticsQuantum mechanicsCell biologyBiologyMicro and Nano RoboticsMicrofluidic and Bio-sensing TechnologiesModular Robots and Swarm Intelligence
Data-Driven Discovery of Active Nematic Hydrodynamics | Litcius