Statistical Physics Meets Machine Learning III

Presentations

• Symmetry-equivariant Neural Networks for Understanding and Designing Physical Systems: Advances, Challenges, and Opportunities

  ORAL · Invited

  March 7, 2024, 3:00 PM – March 7, 2024, 3:36 PM

  Presenters

  • Tess E Smidt

    • MIT

  Authors

  • Tess E Smidt

    • MIT

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• Stochastic force inference via density estimation

  ORAL

  March 7, 2024, 3:36 PM – March 7, 2024, 3:48 PM

  Publication: https://arxiv.org/pdf/2310.02366.pdf

  Presenters

  • Victor Chardès

    • Flatiron Institute

  Authors

  • Victor Chardès

    • Flatiron Institute

  • Suryanarayana Maddu

    • Flatiron Institute

  • Michael J Shelley

    • Flatiron Institute (Simons Foundation)

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• Ab initio uncertainty quantification in scattering analysis of microscopy

  ORAL

  March 7, 2024, 3:48 PM – March 7, 2024, 4:00 PM

  Publication: Gu, M., He, Y., Liu, X., & Luo, Y. (2023). Ab initio uncertainty quantification in scattering analysis of microscopy. arXiv preprint arXiv:2309.02468.

  Presenters

  • Mengyang Gu

    • University of California, Santa Barbara

  Authors

  • Mengyang Gu

    • University of California, Santa Barbara

  • Yue He

    • University of California, Santa Barbara

  • Xubo Liu

    • University of California, Santa Barbara

  • Yimin Luo

    • Yale University

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• Combining physics with multi-fidelity computation for improved Bayesian active learning based exploration over lattice Hamiltonian system

  ORAL

  March 7, 2024, 4:00 PM – March 7, 2024, 4:12 PM

  Presenters

  • Arpan Biswas

    • Oak Ridge National Lab

  Authors

  • Arpan Biswas

    • Oak Ridge National Lab

  • Sai Mani Prudhvi Valleti

    • Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville

  • Rama K Vasudevan

    • Oak Ridge National Laboratory
    • Oak Ridge National Lab

  • Sergei V Kalinin

    • University of Tennessee

  • Maxim Ziatdinov

    • Oak Ridge National Lab

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• Characterizing Inference of Non-reciprocal Connections in the Kinetic Ising Model

  ORAL

  March 7, 2024, 4:12 PM – March 7, 2024, 4:24 PM

  Presenters

  • Peter Fields

    • University of Chicago

  Authors

  • Peter Fields

    • University of Chicago

  • Cheyne Weis

    • University of Chicago

  • Stephanie E Palmer

    • University of Chicago

  • Peter Littlewood

    • University of Chicago

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• Abstract Withdrawn

  ORAL · Withdrawn

  March 7, 2024, 4:24 PM – March 7, 2024, 4:36 PM

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• Data-Enabled Coarse-Graining of Confined Simple Liquids

  ORAL

  March 7, 2024, 4:36 PM – March 7, 2024, 4:48 PM

  Publication: 1. Nadkarni, Ishan, Haiyi Wu, and Narayana R. Aluru. "Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement." Journal of Chemical Theory and Computation (2023).

  Presenters

  • Ishan M Nadkarni

    • University of Texas at Austin

  Authors

  • Ishan M Nadkarni

    • University of Texas at Austin

  • Haiyi Wu

    • UT austin

  • Narayana R Aluru

    • The University of Texas at Austin

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• Abstract Withdrawn

  ORAL · Withdrawn

  March 7, 2024, 4:48 PM – March 7, 2024, 5:00 PM

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• ABSTRACT WITHDRAWN

  COFFEE_KLATCH

  March 7, 2024, 5:00 PM – March 7, 2024, 5:12 PM

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• Self-supervised deep learning for intense charged particle beam dynamics with hard physics constraints

  ORAL

  March 7, 2024, 5:12 PM – March 7, 2024, 5:24 PM

  Presenters

  • Alexander Scheinker

    • Los Alamos Natl Lab

  Authors

  • Alexander Scheinker

    • Los Alamos Natl Lab

  • Reeju Pokharel

    • Los Alamos National Laboratory

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• Learning biophysical energy functions from protein structure data with physically-informed equivariant neural networks

  ORAL

  March 7, 2024, 5:24 PM – March 7, 2024, 5:36 PM

  Presenters

  • Kevin A Borisiak

    • University of Washington

  Authors

  • Kevin A Borisiak

    • University of Washington

  • Armita Nourmohammad

    • University of Washington

  • Michael N Pun

    • University of Washington

  • Gian Marco Visani

    • University of Washington

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