Data-driven discovery of active nematic hydrodynamics

Abstract

Two-dimensional active nematics are often modeled using phenomenological continuum theories that describe the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistically accurate description of the experiments, the identification of the relevant terms in the PDEs and their parameters is usually indirect. Here, we adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from the spatio-temporal director and velocity data, via sparse fitting of the coarse-grained fields onto generic low order PDEs. We test the method extensively on computational models, and then apply it to data from experiments on microtubule-based active nematics. Thereby, we identify the optimal models for microtubule-based active nematics, along with the relevant phenomenological parameters. We find that the dynamics of the orientation field are largely governed by its coupling to the underlying flow, with free-energy gradients playing a negligible role. Furthermore, by fitting the flow equation to experimental data, we estimate a key parameter quantifying the `activity' of the nematic.

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Cited by in corpus (13)

• Active phase separation: new phenomenology from non-equilibrium physics
• Soft Metamaterials: Adaptation and Intelligence
• Rectified Rotational Dynamics of Mobile Inclusions in Two-Dimensional Active Nematics
• Probing active nematics with in-situ microfabricated elastic inclusions
• Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)
• Spontaneous rotation of active droplets in two and three dimensions
• Theory of Nonequilibrium Multicomponent Coexistence
• Closed-loop control of active nematic flows
• Theory of Nonequilibrium Coexistence with Coupled Conserved and Nonconserved Order Parameters
• Interfacial instability of confined 3D active droplets
• Active Cahn-Hilliard theory for nonequilibrium phase separation: quantitative macroscopic predictions and a microscopic derivation
• Learning general pair interactions between self-propelled particles
• Estimation of spatial and time scales of collective behaviors of active matters through learning hydrodynamic equations from particle dynamics