Predicting Nonlinear and Complex Systems with Machine Learning I

Presentations

• A framework for seismic risk policy design and the assessment of avalanche-like event prediction in knitted fabric.

  ORAL

  March 16, 2022, 9:00 AM – March 16, 2022, 9:12 AM

  Presenters

  • Adèle Douin

    CNRS

  Authors

  • Adèle Douin

    CNRS

  • Frédéric Lechenault

    CNRS

  • Jean-Phillipe Bruneton

    LIED

  View abstract →

• Can graph neural network infer knot invariants?

  ORAL

  March 16, 2022, 9:12 AM – March 16, 2022, 9:24 AM

  Presenters

  • Changyeob Baek

    Harvard University

  Authors

  • Changyeob Baek

    Harvard University

  • Christopher H Rycroft

    Harvard University

  View abstract →

• Building Deep Learning Architectures for Physics, Chemistry, and Biology with Geometric Algebra

  ORAL

  March 16, 2022, 9:24 AM – March 16, 2022, 9:36 AM

  Publication: https://arxiv.org/abs/2110.02393

  Presenters

  • Matthew P Spellings

    Vector Institute for Artificial Intellig

  Authors

  • Matthew P Spellings

    Vector Institute for Artificial Intellig

  View abstract →

• Optimal control of quantum thermal machines using machine learning

  ORAL

  March 16, 2022, 9:36 AM – March 16, 2022, 9:48 AM

  Publication: https://arxiv.org/abs/2108.12441

  Presenters

  • Ilia Khait

    Univ of Toronto

  Authors

  • Ilia Khait

    Univ of Toronto

  • Juan Carrasquilla

    Vector Institute for Artificial Intelligence

  • Dvira Segal

    University of Toronto

  View abstract →

• On Explaining the Surprising Success of Reservoir Computing Forecaster of Chaos? The Universal Machine Learning Dynamical System with Contrasts to VAR and DMD

  ORAL · Invited

  March 16, 2022, 9:48 AM – March 16, 2022, 10:24 AM

  Presenters

  • Erik Bollt

    Clarkson University

  Authors

  • Erik Bollt

    Clarkson University

  View abstract →

• Programming Memories and Computations in Recurrent Neural Networks Without Training

  ORAL

  March 16, 2022, 10:24 AM – March 16, 2022, 10:36 AM

  Publication: Kim, J. Z., Lu, Z., Nozari, E., Pappas, G. J., & Bassett, D. S. (2021). Teaching recurrent neural networks to infer global temporal structure from local examples. Nature Machine Intelligence, 3(4), 316-323.

  Presenters

  • Jason Z Kim

    University of Pennsylvania

  Authors

  • Jason Z Kim

    University of Pennsylvania

  • Zhixin Lu

    University of Pennsylvania

  • Danielle S Bassett

    University of Pennsylvania

  View abstract →

• Learning the hidden rheology of complex fluids through MF-RhIGNet: Multi Fidelity Rheology-Informed Graph Neural Network

  ORAL

  March 16, 2022, 10:36 AM – March 16, 2022, 10:48 AM

  Publication: https://doi.org/10.1038/s41598-021-91518-3 <br>https://doi.org/10.1122/8.0000138

  Presenters

  • Mohammadamin Mahmoudabadbozchelou

    Northeastern University

  Authors

  • Mohammadamin Mahmoudabadbozchelou

    Northeastern University

  • Krutarth M Kamani

    University of Illinois at Urbana-Champaign

  • Simon A Rogers

    University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champai

  • Safa Jamali

    Northeastern University

  View abstract →

• Rheology-informed neural networks for non-local granular flows

  ORAL

  March 16, 2022, 10:48 AM – March 16, 2022, 11:00 AM

  Presenters

  • Milad Saadat

    Northeastern University

  Authors

  • Milad Saadat

    Northeastern University

  • Mohammadamin Mahmoudabadbozchelou

    Northeastern University

  • Safa Jamali

    Northeastern University

  View abstract →

• Machine Learning for Robot Locomotion in Flowable Materials

  ORAL

  March 16, 2022, 11:00 AM – March 16, 2022, 11:12 AM

  Presenters

  • Daniel Soto

    Georgia Institute of Technology

  Authors

  • Daniel Soto

    Georgia Institute of Technology

  • Andras Karsai

    Georgia Institute of Technology

  • Daniel I Goldman

    georgia tech, Georgia Institute of Technology, Georgia Institute of Technology, Atlalta, GA, Georgia Tech

  • Sehoon Ha

    Georgia Institute of Technology

  • Tingnan Zhang

    Google

  View abstract →

• Predicting a clog: finding signatures of clog formation in hopper flow using machine learning methods

  ORAL

  March 16, 2022, 11:12 AM – March 16, 2022, 11:24 AM

  Presenters

  • Jesse M Hanlan

    University of Pennsylvania

  Authors

  • Jesse M Hanlan

    University of Pennsylvania

  • Sam J Dillavou

    University of Pennsylvania

  • Andrea J Liu

    University of Pennsylvania

  • Douglas J Durian

    University of Pennsylvania

  View abstract →

• Using analogical reasoning to build transferable models

  ORAL

  March 16, 2022, 11:24 AM – March 16, 2022, 11:36 AM

  Publication: C. Petrie, C. Anderson, C. Maekawa, T. Maekawa, and M. K. Transtrum (2021). The supremum principle selects simple, transferable models. Manuscript submitted for publication.

  Presenters

  • Cody L Petrie

    Brigham Young University

  Authors

  • Cody L Petrie

    Brigham Young University

  • Christian N Anderson

    Brigham Young University

  • Casie Maekawa

    Brigham Young University

  • Travis Maekawa

    Brigham Young University

  • Mark K Transtrum

    Brigham Young University

  View abstract →

• Evidence for Griffiths Phase Criticality in Residual Neural Networks

  ORAL

  March 16, 2022, 11:36 AM – March 16, 2022, 11:48 AM

  Presenters

  • Maxwell Anderson

    Cornell University

  Authors

  • Logan G Wright

    Cornell University, Cornell University & NTT Research

  • Maxwell Anderson

    Cornell University

  • Peter L McMahon

    Cornell University, Stanford Univ

  View abstract →

• Capturing Strain Induced Orthotropy Through a Novel Free Energy Density Definition

  ORAL

  March 16, 2022, 11:48 AM – March 16, 2022, 12:00 PM

  Publication: Planned White Paper: Strain Induced Orthotropy, Thermomechanics, and Nonlinear Viscoelasticity: A Unified Theory

  Presenters

  • Alex G Arzoumanidis

    Psylotech, Inc.

  Authors

  • Alex G Arzoumanidis

    Psylotech, Inc.

  View abstract →