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Machine learning forecasting of active nematics
Soft Matter ( IF 3.4 ) Pub Date : 2020-11-14 , DOI: 10.1039/d0sm01316a
Zhengyang Zhou 1, 2, 3 , Chaitanya Joshi 2, 3, 4 , Ruoshi Liu 2, 3, 4 , Michael M. Norton 2, 3, 4 , Linnea Lemma 2, 3, 4 , Zvonimir Dogic 3, 4, 5, 6 , Michael F. Hagan 2, 3, 4 , Seth Fraden 2, 3, 4 , Pengyu Hong 1, 2, 3
Affiliation  

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.

中文翻译:

主动向列的机器学习预测

主动向列是一类极不平衡的材料,其特征在于产生力的各向异性结构的局部取向顺序。预测主动向列动力学的传统方法依赖于流体动力学模型,该模型可以准确描述理想化流量和许多稳态特性,但不能捕获实验主动向列动力学的某些详细动力学信息。我们已经开发出一种深度学习方法,该方法使用卷积长短期记忆(ConvLSTM)算法来自动学习和预测主动向列动力学。我们展示了纯粹的数据驱动方法,用于可扩展微管束的二维无限制主动向列相的实验,以及来自主动向列相的数值模拟的数据。
更新日期:2020-11-22
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