Machine Learning in Nonlinear Physics and Mechanics

FOCUS · F54

Presentations

  • Predicting the dynamics of crumpling with machine learning

    ORAL

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    Presenters

    • Christopher Rycroft

      SEAS, Harvard Univ, Harvard University, SEAS, Harvard University, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University, Applied Mathematics, Harvard University

    Authors

    • Christopher Rycroft

      SEAS, Harvard Univ, Harvard University, SEAS, Harvard University, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University, Applied Mathematics, Harvard University

    • Jordan Hoffmann

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Jovana Andrejevic

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Lisa Lee

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Shmuel Rubinstein

      SEAS, John A Paulson School of Engineering and Applied Sciences, Harvard University, Applied Physics, Harvard Univ, SEAS, Harvard Univ, Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University, SEAS, Harvard University, Harvard University

    View abstract →

  • Detection and Characterization Techniques for Signatures of Crumpling History

    ORAL

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    Presenters

    • Jovana Andrejevic

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    Authors

    • Jovana Andrejevic

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Jordan Hoffmann

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Lisa Lee

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Shmuel Rubinstein

      SEAS, John A Paulson School of Engineering and Applied Sciences, Harvard University, Applied Physics, Harvard Univ, SEAS, Harvard Univ, Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University, SEAS, Harvard University, Harvard University

    • Christopher Rycroft

      SEAS, Harvard Univ, Harvard University, SEAS, Harvard University, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University, Applied Mathematics, Harvard University

    View abstract →

  • Using Machine Learning to Understand the Evolution of Damage Networks in Thin Sheets

    ORAL

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    Presenters

    • Lisa Lee

      Harvard Univ

    Authors

    • Lisa Lee

      Harvard Univ

    • Jovana Andrejevic

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Jordan Hoffmann

      Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University

    • Christopher Rycroft

      SEAS, Harvard Univ, Harvard University, SEAS, Harvard University, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University, Applied Mathematics, Harvard University

    • Shmuel Rubinstein

      SEAS, John A Paulson School of Engineering and Applied Sciences, Harvard University, Applied Physics, Harvard Univ, SEAS, Harvard Univ, Harvard Univ, Paulson School of Engineering and Applied Sciences, Harvard University, SEAS, Harvard University, Harvard University

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  • Identifying Structural Defects in Disordered Packings of Elongated Particles using Machine Learning

    ORAL

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    Presenters

    • Matt Harrington

      University of Pennsylvania

    Authors

    • Matt Harrington

      University of Pennsylvania

    • Andrea Liu

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

    • Douglas Durian

      Department of Physics & Astronomy, University of Pennsylvania, Department of Physics and Astronomy, Univ of Pennsylvania, University of Pennsylvania

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  • Reservoir computer predictions for the Three Meter magnetic field time evolution

    ORAL

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    Presenters

    • Artur Perevalov

      Physics, IREAP, University of Maryland College Park

    Authors

    • Artur Perevalov

      Physics, IREAP, University of Maryland College Park

    • Ruben Rojas Garcia

      Physics, IREAP, University of Maryland College Park

    • Itamar Shani

      Physics, IREAP, University of Maryland College Park

    • Brian Hunt

      Institute for Physical Sciences and Technology, University of Maryland College Park

    • Daniel Lathrop

      Physics, University of Maryland, Physics, IREAP, University of Maryland College Park

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  • Using image Super-Resolution techniques as a coarse-graining method for physical systems

    ORAL

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    Presenters

    • Yohai Bar-Sinai

      SEAS, Harvard University, Harvard Univ

    Authors

    • Yohai Bar-Sinai

      SEAS, Harvard University, Harvard Univ

    • Michael Brenner

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

    • Pascal Getreuer

      Google Research, Google

    • Jason Hickey

      Google Research, Google

    • Stephan Hoyer

      Google Research, Google

    • Peyman Milanfar

      Google Research, Google

    View abstract →

  • Predicting Emergent Crystalline Structural Order from Building Block Geometry

    ORAL

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    Presenters

    • Yina Geng

      Univ of Michigan - Ann Arbor

    Authors

    • Yina Geng

      Univ of Michigan - Ann Arbor

    • Greg Van Anders

      Department of Physics, University of Michigan, Univ of Michigan - Ann Arbor, Department of Physics, Univ of Michigan - Ann Arbor, University Michigan

    • Sharon Glotzer

      Chemical Engineering, Univ of Michigan - Ann Arbor, Univ of Michigan - Ann Arbor, Department of Chemical Engineering, University of Michigan - Ann Arbor, Department of Chemical Engineering, University of Michigan, Chemical Engineering, University of Michigan, Department of Chemical Engineering, Univ of Michigan - Ann Arbor

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  • Intelligent, autonomous parameter space exploration of self-assembly simulations

    ORAL

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    Presenters

    • Matthew Spellings

      Chemical Engineering, University of Michigan

    Authors

    • Matthew Spellings

      Chemical Engineering, University of Michigan

    • Alexey Feofanov

      University of Innsbruck, University of Waterloo, Korea University, Okinawa Institute of Science and Technology, University of California - Los Angeles, The University of Manchester, University of Puerto Rico at Humacao, Department of Physics & Electronics, University of Puerto Rico at Cayey, Department of Mathematics-Physics, Oak Ridge National Lab, Max Planck Institute for Chemical Physics of Solids, Department of Physics, University of Puerto Rico, Electrical Engineering Department, University of Arkansas, Department of Physics, University of Arkansas, School of Basic Sciences at IIT Mandi, H.P., India, Computational Biology, Flatiron Institute, Physics, Hong Kong Univ of Sci & Tech, University of California, Los Angeles, Max Planck Inst, Institute for Theoretical Physics, University of Cologne, Department of Physics, Simon Fraser University, Deutsches Elektronen Synchrotron (DESY), Institut fur Theoretische Physik, Univerisitat zu Berlin, Institut fur Physik, Univerisitat zu Berlin, Plymouth State University, The Graduate Center, CUNY, Nordita, KTH Royal Institute of Technology and Stockholm University, Univ of Connecticut - Storrs, Univ Stuttgart, University of Chicago, University of Texas at El Paso, University of Tulsa, California Institute of Technology, Georgia Institute of Technology, Universite Paris Diderot, Laboratoire MPQ, Universita di Trento, BEC Center, ICTP Trieste, Universita di Pisa, Inst of Physics Academia Sinica, Batelle, Cal State Univ- San Bernardino, Chemical Engineering, University of Michigan, QCD Labs, Department of Applied Physics, Aalto University, Yale University, MIT, Harvard Univ, Chemical & Environmental Engineering, University of California, Riverside, University of Frankfurt, Germany, University of Hamburg, Germany, Naval Research Laboratory, Cornell Univ, National Institute for Material Science, U.S. Naval Research Laboratory, Washington DC, Materials Engineering, University of Santa Barbara, Institute of Physics, Chinese Academy of Sciences, Univ of Texas, Arlington, MIT Lincoln Laboratory, University of Sydney, Iowa State University, Purdue University, Kansas State University, University of Maryland, John Hopkins University, Universite de Sherbrooke, Physics, Konkuk University, Perimeter Institute, University of Waterloo, D-Wave, San Jose State University, Université de Sherbrooke, Institute of Physics, EPFL - Lausanne​

    • Sharon Glotzer

      Chemical Engineering, Univ of Michigan - Ann Arbor, Univ of Michigan - Ann Arbor, Department of Chemical Engineering, University of Michigan - Ann Arbor, Department of Chemical Engineering, University of Michigan, Chemical Engineering, University of Michigan, Department of Chemical Engineering, Univ of Michigan - Ann Arbor

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  • Distilling the logic of behavioral dynamics using automated inference

    ORAL

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    Presenters

    • Bryan Daniels

      ASU–SFI Center for Biosocial Complex Systems, Arizona State University

    Authors

    • Bryan Daniels

      ASU–SFI Center for Biosocial Complex Systems, Arizona State University

    • William Ryu

      University of Toronto

    • Ilya Nemenman

      Emory Univ, Emory University, Department of Physics, Department of Biology, Emory University

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  • Computational tools for data-driven design of soft robots

    ORAL

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    Presenters

    • Mohammad Khalid Jawed

      University of California, Los Angeles, Department of Mechanical and Aerospace Engineering, Univ of California - Los Angeles

    Authors

    • Mohammad Khalid Jawed

      University of California, Los Angeles, Department of Mechanical and Aerospace Engineering, Univ of California - Los Angeles

    • Xiaonan Huang

      Mechanical Engineering, Carnegie Mellon University, Department of Mechanical Engineering, Carnegie Mellon University

    • Amarbold Batzorig

      California Institute of Technology, Department of Mechanical Engineering, Carnegie Mellon University

    • Carmel Majidi

      Mechanical Engineering, Carnegie Mellon University, Department of Mechanical Engineering, Carnegie Mellon University

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  • Deep Learning Physical Phenomena

    ORAL

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    Presenters

    • Joseph Gomes

      Chemistry, Stanford University

    Authors

    • Joseph Gomes

      Chemistry, Stanford University

    • Amir Barati Farimani

      Univ of Illinois - Urbana, Chemistry, Stanford University

    • Vijay Pande

      Chemistry, Stanford University

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  • Visualizing theory space: Isometric embedding of probabilistic predictions, from the Ising model to the cosmic microwave background

    ORAL

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    Presenters

    • Katherine Quinn

      Physics, Cornell University

    Authors

    • Katherine Quinn

      Physics, Cornell University

    • Francesco De Bernardis

      Physics, Cornell University

    • Michael Niemack

      Physics, Cornell University

    • James Sethna

      Cornell University, Laboratory of Atomic and Solid State Physics, Cornell University, Physics, Cornell University

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