Machine learning forecasting of active nematics.

Soft Matter
Zhengyang ZhouPengyu Hong

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.

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Citations

Mar 13, 2021·Proceedings of the National Academy of Sciences of the United States of America·Anna Frishman, Kinneret Keren
Sep 19, 2021·Proceedings of the National Academy of Sciences of the United States of America·Kristian ThijssenTyler N Shendruk

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