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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