Statistical Physics Meets Machine Learning III
FOCUS · W28 · ID: 2154368
Presentations
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Symmetry-equivariant Neural Networks for Understanding and Designing Physical Systems: Advances, Challenges, and Opportunities
ORAL · Invited
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Presenters
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Tess E Smidt
MIT
Authors
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Tess E Smidt
MIT
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Stochastic force inference via density estimation
ORAL
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Publication: https://arxiv.org/pdf/2310.02366.pdf
Presenters
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Victor Chardès
Flatiron Institute
Authors
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Victor Chardès
Flatiron Institute
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Suryanarayana Maddu
Flatiron Institute
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Michael J Shelley
Flatiron Institute (Simons Foundation)
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Ab initio uncertainty quantification in scattering analysis of microscopy
ORAL
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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
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Mengyang Gu
University of California, Santa Barbara
Authors
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Mengyang Gu
University of California, Santa Barbara
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Yue He
University of California, Santa Barbara
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Xubo Liu
University of California, Santa Barbara
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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
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Presenters
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Arpan Biswas
Oak Ridge National Lab
Authors
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Arpan Biswas
Oak Ridge National Lab
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Sai Mani Prudhvi Valleti
Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville
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Rama K Vasudevan
Oak Ridge National Laboratory, Oak Ridge National Lab
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Sergei V Kalinin
University of Tennessee
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Maxim Ziatdinov
Oak Ridge National Lab
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Characterizing Inference of Non-reciprocal Connections in the Kinetic Ising Model
ORAL
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Presenters
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Peter Fields
University of Chicago
Authors
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Peter Fields
University of Chicago
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Cheyne Weis
University of Chicago
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Stephanie E Palmer
University of Chicago
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Peter Littlewood
University of Chicago
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Abstract Withdrawn
ORAL Withdrawn
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Data-Enabled Coarse-Graining of Confined Simple Liquids
ORAL
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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
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Ishan M Nadkarni
University of Texas at Austin
Authors
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Ishan M Nadkarni
University of Texas at Austin
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Haiyi Wu
UT austin
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Narayana R Aluru
The University of Texas at Austin
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Abstract Withdrawn
ORAL Withdrawn
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ABSTRACT WITHDRAWN
COFFEE_KLATCH
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Self-supervised deep learning for intense charged particle beam dynamics with hard physics constraints
ORAL
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Presenters
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Alexander Scheinker
Los Alamos Natl Lab
Authors
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Alexander Scheinker
Los Alamos Natl Lab
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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
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Presenters
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Kevin A Borisiak
University of Washington
Authors
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Kevin A Borisiak
University of Washington
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Armita Nourmohammad
University of Washington
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Michael N Pun
University of Washington
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Gian Marco Visani
University of Washington
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