Machine learning forecasting of active nematics. Issue 3 (21st November 2020)

• Record Type: Journal Article
• Authors: Zhou, Zhengyang; Joshi, Chaitanya; Liu, Ruoshi; Norton, Michael M.; Lemma, Linnea; Dogic, Zvonimir; Hagan, Michael F.; Fraden, Seth; Hong, Pengyu
• Is Part Of: Soft matter. Volume 17:Issue 3(2021)
• Page Start: 738
• Page End: 747
• Publication Date: 2020-11-21
• Subjects: Soft condensed matter -- Periodicals; 530.413
• DOI: 10.1039/d0sm01316a
• Languages: English
• ISSNs: 1744-683X

Abstract

Abstract : Our model is unrolled to map an input orientation sequence (from time t -8 to t -1) to an output one ( t, t + 1…) with trajectray tracing. Cyan labels are −1/2 defect while purple ones are +1/2. 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.