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Machine learning active-nematic hydrodynamics

ORAL

Abstract

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to determine from microscopics. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields encoding the distribution of the energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatio-temporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. By analyzing microtubule-kinesin and actin-myosin experiments as computer vision problems, our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as ATP or motor concentration. They 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 networks with residual architecture. Our work paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems even when no knowledge of the underlying dynamics exists.

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Presenters

  • Jonathan Colen

    University of Chicago

Authors

  • Jonathan Colen

    University of Chicago

  • Ming Han

    University of Chicago

  • Rui Zhang

    University of Chicago, The Hong Kong University of Science and Technology

  • Steven Redford

    University of Chicago

  • Linnea M Lemma

    Physics, University of California, Santa Barbara

  • Link Morgan

    Physics, University of California, Santa Barbara

  • Paul Ruijgrok

    Stanford University

  • Raymond Adkins

    Physics, University of California, Santa Barbara, University of California, Santa Barbara

  • Zev Bryant

    Stanford University

  • Zvonimir Dogic

    Physics, University of California, Santa Barbara, University of California, Santa Barbara, University of California at Santa Barbara, Santa Barbara, University of California, Santa Barbara, Harvard University, Brandeis University

  • Margaret Gardel

    University of Chicago, Department of Physics, University of Chicago

  • Juan De Pablo

    University of Chicago, Molecular Engineering, University of Chicago, Institute for Molecular Engineering, University of Chicago, The Pritzker School of Molecular Engineering, University of Chicago

  • Vincenzo Vitelli

    University of Chicago, Department of Physics, University of Chicago, The University of Chicago