Machine learning active-nematic hydrodynamics
Top Cited Papers
- 2 March 2021
- journal article
- research article
- Published by Proceedings of the National Academy of Sciences in Proceedings of the National Academy of Sciences of the United States of America
- Vol. 118 (10)
- https://doi.org/10.1073/pnas.2016708118
Abstract
Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.Funding Information
- Army Research Office (W911NF-19-1-026)
- Chicago MRSEC (DMR-2011854)
- Chicago MRSEC Kadanoff-Rice Postdoctoral Fellowship (DMR-2011854)
- National Institute of Health (GM114627)
- National Science Foundation (DMR-1905675)
- National Science Foundation (DMR-1905675)
- Army Research Office (W911NF1410403)
This publication has 71 references indexed in Scilit:
- Spontaneous motion in hierarchically assembled active matterNature, 2012
- Deciphering Interactions in Moving Animal GroupsPLoS Computational Biology, 2012
- Two Distinct Modes of Processive Kinesin Movement in Mixtures of ATP and AMP-PNPThe Journal of general physiology, 2007
- Hydrodynamics and Rheology of Active Liquid Crystals: A Numerical InvestigationPhysical Review Letters, 2007
- Spontaneous flow transition in active polar gelsEurophysics Letters, 2005
- Lattice Boltzmann algorithm for three–dimensional liquid–crystal hydrodynamicsPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2004
- Purification of brain tubulin through two cycles of polymerization–depolymerization in a high-molarity bufferProtein Expression and Purification, 2003
- Hydrodynamic Fluctuations and Instabilities in Ordered Suspensions of Self-Propelled ParticlesPhysical Review Letters, 2002
- Lattice Boltzmann simulations of liquid crystal hydrodynamicsPhysical Review E, 2001
- The Physics of Liquid CrystalsPhysics Today, 1995