  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.
