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Title: Machine learning forecasting of active nematics
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.  more » « less
Award ID(s):
1855914 1810077 1920147
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Soft Matter
Page Range / eLocation ID:
738 to 747
Medium: X
Sponsoring Org:
National Science Foundation
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