Machine Learning in Nonlinear Physics and Mechanics

Presentations

• A case study in neural networks for scientific data: generating atomic structures

  Invited

  March 5, 2019, 2:30 PM – March 5, 2019, 3:06 PM

  Presenters

  • Tess Smidt

    • Computational Research Division, Lawrence Berkeley National Laboratory

  Authors

  • Tess Smidt

    • Computational Research Division, Lawrence Berkeley National Laboratory

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• A computational model for crumpled thin sheets to complement data-driven machine learning

  ORAL

  March 5, 2019, 3:06 PM – March 5, 2019, 3:18 PM

  Presenters

  • Jovana Andrejevic

    • Harvard University

  Authors

  • Jovana Andrejevic

    • Harvard University

  • Jordan Hoffmann

    • Harvard University

  • Yohai Bar-Sinai

    • Harvard University

  • Lisa Lee

    • Harvard University

  • Shruti Mishra

    • Harvard University

  • Shmuel Rubinstein

    • School of Engineering and Applied Sciences, Harvard University
    • Harvard SEAS
    • SMRlab, Harvard University
    • Harvard University
    • SEAS, Harvard University

  • Christopher Rycroft

    • SEAS, Harvard University
    • Harvard University
    • Paulson School of Engineering and Applied Sciences, Harvard University

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• Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

  ORAL

  March 5, 2019, 3:18 PM – March 5, 2019, 3:30 PM

  Presenters

  • Lisa Lee

    • Harvard University

  Authors

  • Lisa Lee

    • Harvard University

  • Jordan Hoffmann

    • Harvard University

  • Yohai Bar-Sinai

    • Harvard University

  • Jovana Andrejevic

    • Harvard University

  • Shruti Mishra

    • Harvard University

  • Shmuel Rubinstein

    • School of Engineering and Applied Sciences, Harvard University
    • Harvard SEAS
    • SMRlab, Harvard University
    • Harvard University
    • SEAS, Harvard University

  • Christopher Rycroft

    • SEAS, Harvard University
    • Harvard University
    • Paulson School of Engineering and Applied Sciences, Harvard University

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• Search and design of stretchable graphene kirigami using convolutional neural networks

  ORAL

  March 5, 2019, 3:30 PM – March 5, 2019, 3:42 PM

  Presenters

  • Paul Hanakata

    • Boston University

  Authors

  • Paul Hanakata

    • Boston University

  • Ekin Dogus Cubuk

    • Stanford University
    • Google Brain

  • David K Campbell

    • Boston University
    • Boston Univ
    • Department of Physics, Osaka University
    • Department of Physics, Boston Universtiy
    • Physics, Boston University

  • Harold Park

    • Boston University

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• Clog prediction in granular hoppers using machine learning methods

  ORAL

  March 5, 2019, 3:42 PM – March 5, 2019, 3:54 PM

  Presenters

  • Jesse Hanlan

    • Department of Physics and Astronomy, University of Pennsylvania

  Authors

  • Jesse Hanlan

    • Department of Physics and Astronomy, University of Pennsylvania

  • Douglas Durian

    • University of Pennsylvania
    • Department of Physics and Astronomy, University of Pennsylvania

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• Tracking Topological Defects in 2D Active Nematics Using Convolutional Neural Networks

  ORAL

  March 5, 2019, 3:54 PM – March 5, 2019, 4:06 PM

  Presenters

  • Ruoshi Liu

    • Brandeis University

  Authors

  • Ruoshi Liu

    • Brandeis University

  • Pengyu Hong

    • Brandeis University

  • Michael Norton

    • Brandeis University
    • Physics, Brandeis University

  • Seth Fraden

    • Physics, Brandeis University
    • Brandeis University
    • Physics Department, Brandeis University
    • Department of Physics, Brandeis University

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• Connecting structure and dynamics in a model of confluent cell tissues using machine learning

  ORAL

  March 5, 2019, 4:06 PM – March 5, 2019, 4:18 PM

  Presenters

  • Tristan A Sharp

    • University of Pennsylvania

  Authors

  • Tristan A Sharp

    • University of Pennsylvania

  • Andrea J Liu

    • University of Pennsylvania

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• Design and learning in multi-stable mechanical networks

  ORAL

  March 5, 2019, 4:18 PM – March 5, 2019, 4:30 PM

  Presenters

  • Menachem Stern

    • University of Chicago

  Authors

  • Menachem Stern

    • University of Chicago

  • Matthew Pinson

    • University of Chicago

  • Arvind Murugan

    • James Franck Institute, University of Chicago
    • James Franck Institute
    • physics, University of Chicago
    • University of Chicago

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• DropNet : A neural network solution to flow instabilities.

  ORAL

  March 5, 2019, 4:30 PM – March 5, 2019, 4:42 PM

  Presenters

  • Maxime Lavech du Bos

    • CBE, Princeton University

  Authors

  • Maxime Lavech du Bos

    • CBE, Princeton University

  • Joel Marthelot

    • CBE, Princeton University
    • Princeton University

  • Pierre-Thomas Brun

    • CBE, Princeton University

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• Visualizing probabilistic models and data with Intensive Principal Component Analysis (InPCA)

  ORAL

  March 5, 2019, 4:42 PM – March 5, 2019, 4:54 PM

  Presenters

  • Katherine Quinn

    • Cornell University

  Authors

  • Katherine Quinn

    • Cornell University

  • Colin Clement

    • Cornell University

  • Francesco De Bernardis

    • Cornell University

  • Michael D Niemack

    • Cornell University

  • James Patarasp Sethna

    • Cornell University

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• Physical Symmetries Embedded in Neural Networks

  ORAL

  March 5, 2019, 4:54 PM – March 5, 2019, 5:06 PM

  Presenters

  • Marios Mattheakis

    • Harvard University

  Authors

  • Marios Mattheakis

    • Harvard University

  • David Sondak

    • Harvard University

  • Pavlos Protopapas

    • Harvard University

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• Maximizing thermal efficiency of heat engines using neuroevolutionary strategies for reinforcement learning

  ORAL

  March 5, 2019, 5:06 PM – March 5, 2019, 5:18 PM

  Presenters

  • Christopher Beeler

    • University of Ontario, Institute of Technology

  Authors

  • Christopher Beeler

    • University of Ontario, Institute of Technology

  • Uladzimir Yahorau

    • University of Ontario, Institute of Technology

  • Rory Coles

    • University of Ontario, Institute of Technology
    • University of Ontario Institute of Technology

  • Kyle Mills

    • University of Ontario, Institute of Technology

  • Isaac Tamblyn

    • University of Ontario Institute of Technology, University of Ottawa, and National Research Council of Canada
    • University of Ontario Institute of Technology, National Research Council of Canada
    • National Research Council of Canada

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