Machine learning forecasting of active nematics.

@article{Zhou2020MachineLF,
  title={Machine learning forecasting of active nematics.},
  author={Zhengyang Zhou and Chaitanya Joshi and Ruoshi Liu and Michael M. Norton and Linnea M. Lemma and Zvonimir Dogic and Michael F. Hagan and Seth Fraden and Pengyu Hong},
  journal={Soft matter},
  year={2020}
}
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… Expand
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Submersed micropatterned structures control active nematic flow, topology, and concentration
TLDR
An easy-to-implement technique to achieve tunable, local control over active nematic films, the preeminent example of active fluids, is reported and a convenient, highly tunable method for controlling flow, topology, and composition within active films is proposed. Expand
Topological active matter
In this review, we summarize recent progress in understanding the role and relevance of topological excitations in a special category of systems called active matter. Active matter is a class of… Expand
Symmetry, Thermodynamics and Topology in Active Matter
Mark J. Bowick, Nikta Fakhri, M. Cristina Marchetti, and Sriram Ramaswamy Kavli Institute for Theoretical Physics, University of California Santa Barbara, Santa Barbara, CA 93106, USA Department of… Expand
Learning active nematics one step at a time

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