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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9 Citations
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9 Citations

Physically-informed data-driven modeling of active nematics

A continuum description is essential for understanding a variety of collective phenomena in active matter. However, building quantitative continuum models of active matter from first principles can be…

Submersed micropatterned structures control active nematic flow, topology, and concentration

An easy-to-implement technique to achieve tunable, local control over active nematic films, the preeminent example of active fluids, is reported and a convenient, highly tunable method for controlling flow, topology, and composition within active films is proposed.

Competing instabilities reveal how to rationally design and control active crosslinked gels

This paper investigates the origin of two distinct instabilities in active gels of biopolymers and molecular motors, and shows how to rationally design and control active materials with targeted elasticity and activity.

Kinesin and Myosin Motors Compete to Drive Rich Multi-Phase Dynamics in Programmable Cytoskeletal Composites

Actin-microtubule composites are engineer, driven by kinesin and myosin motors and tuned by crosslinkers, that restructure into diverse morphologies from interpenetrating filamentous networks to de-mixed amorphous clusters.

Topological active matter

In this review, we summarize recent progress in understanding the role and relevance of topological excitations in a special category of systems called active matter. Active matter is a class of…

Variational methods and deep Ritz method for active elastic solids.

Variational methods have been widely used in soft matter physics for both static and dynamic problems. These methods are mostly based on two variational principles: the variational principle of…

Topological defects govern mesenchymal condensations, offering a morphology-based tool to predict cartilage differentiation

This work shows that swirl-pattern quantification provides a novel and powerful tool to predict efficacy of bm-MSCs for in-vitro cartilage regeneration, and suggests that swirl pattern quantification via image analysis can be used to predict differentiation outcome, in context of regenerative cell therapy.

Symmetry, Thermodynamics, and Topology in Active Matter

Mark J. Bowick, Nikta Fakhri, M. Cristina Marchetti, and Sriram Ramaswamy Kavli Institute for Theoretical Physics, University of California Santa Barbara, Santa Barbara, CA 93106, USA Department of…

Learning active nematics one step at a time

The study by Colen et al. (2) applies neural networks as an analysis tool for active-nematic systems, illustrating how these new approaches can provide experimental physicists with better tools to analyze their data.

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