Journal
SOFT MATTER
Volume 17, Issue 3, Pages 738-747Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d0sm01316a
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Funding
- NSF [DMR-MRSEC 2011486, DMR-1420382, NSF-DMR-1810077, DMR-1855914, OAC 1920147]
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Active nematics are materials characterized by local orientational order and anisotropic constitutes of force generation, usually studied using traditional hydrodynamic models. Researchers have developed a deep learning approach using ConvLSTM algorithm to predict the dynamics of active nematics, regardless of traditional models.
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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