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Issue 3, 2021
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Machine learning forecasting of active nematics

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Abstract

Active nematics are a class of far-from-equilibrium materials characterized by local orientational order of force-generating, anisotropic constitutes. Traditional methods for predicting the dynamics of active nematics rely on hydrodynamic models, which accurately describe idealized flows and many of the steady-state properties, but do not capture certain detailed dynamics of experimental active nematics. We have developed a deep learning approach that uses a Convolutional Long-Short-Term-Memory (ConvLSTM) algorithm to automatically learn and forecast the dynamics of active nematics. We demonstrate our purely data-driven approach on experiments of 2D unconfined active nematics of extensile microtubule bundles, as well as on data from numerical simulations of active nematics.

Graphical abstract: Machine learning forecasting of active nematics

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Article information


Submitted
19 Jul 2020
Accepted
05 Nov 2020
First published
14 Nov 2020

Soft Matter, 2021,17, 738-747
Article type
Paper

Machine learning forecasting of active nematics

Z. Zhou, C. Joshi, R. Liu, M. M. Norton, L. Lemma, Z. Dogic, M. F. Hagan, S. Fraden and P. Hong, Soft Matter, 2021, 17, 738
DOI: 10.1039/D0SM01316A

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