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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)738-747
Number of pages10
JournalSoft matter
Volume17
Issue number3
DOIs
StatePublished - Jan 21 2021

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Condensed Matter Physics

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