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… 

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