Machine Learning Forecasting of Active Nematics

Zhengyang Zhou; Chaitanya Joshi; Ruoshi Liu; Michael M. Norton; Linnea Lemma; Zvonimir Dogic; Michael F. Hagan; Seth Fraden; Pengyu Hong

arXiv · 2011.00383

Machine Learning Forecasting of Active Nematics

Zhengyang Zhou·Chaitanya Joshi·Ruoshi Liu·Michael M. Norton·Linnea Lemma·Zvonimir Dogic·Michael F. Hagan·Seth Fraden·Pengyu Hong

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Abstract

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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BibTeX RIS

Zhengyang Zhou, Chaitanya Joshi, Ruoshi Liu, Michael M. Norton, Linnea Lemma, Zvonimir Dogic, Michael F. Hagan, Seth Fraden, Pengyu Hong. 2020-10-31. Machine Learning Forecasting of Active Nematics. https://arxiv.org/abs/2011.00383

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