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Creators/Authors contains: "Liu, Ruoshi"

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  1. ; ; ; ; ; ; ; (, CoRL)
    Free, publicly-accessible full text available November 13, 2025
  2. ; ; ; ; ; ; ; ; (, European Conference on Computer Vision)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; (, Soft Matter)
    null (Ed.)
    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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