Predicting Nonlinear and Complex Systems with Machine Learning
ORAL · A01 · ID: 1067656
Presentations
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Learning dynamics of complex systems from partial observations
ORAL
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Presenters
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George Stepaniants
Massachusetts Institute of Technology MIT
Authors
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George Stepaniants
Massachusetts Institute of Technology MIT
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Alasdair Hastewell
Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT
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Dominic J Skinner
Northwestern University, Massachusetts Institute of Technology MIT
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Jan F Totz
Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT
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Jorn Dunkel
Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT
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Maximizing dynamical systems information embedded in experimental observables of molecules through statistical learning enabled Takens reconstruction
ORAL
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Publication: Learned reconstruction of protein folding trajectories from noisy single-molecule time series, submitted manuscript to JCTC
Presenters
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Maximilian T Topel
University of Chicago
Authors
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Maximilian T Topel
University of Chicago
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Andrew L Ferguson
University of Chicago
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Fractional neural networks for constitutive modeling of complex fluids
ORAL
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Presenters
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Donya Dabiri
Northeastern University
Authors
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Donya Dabiri
Northeastern University
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Milad Saadat
Northeastern University
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Deepak Mangal
Northeastern University
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Safa Jamali
Northeastern University
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Inferring force law in many-particle systems using physics-tailored machine learning
ORAL
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Presenters
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Wentao Yu
Emory University
Authors
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Wentao Yu
Emory University
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Justin C Burton
Emory University
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Ilya M Nemenman
Emory, Emory University
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Eslam Abdelaleem
Emory University
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Dynamics of long-term memory in recurrent neural networks
ORAL
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Publication: Dynamics of long-term memory in recurrent neural networks. Ling-Wei Kong, Junjie Jiang, and Ying-Cheng Lai. (In Preparation)
Presenters
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Ling-Wei Kong
Arizona State University
Authors
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Ling-Wei Kong
Arizona State University
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Junjie Jiang
Xi'an Jiaotong University
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Ying-Cheng Lai
Arizona State University
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Searching for clog formation in hopper flow through comparative machine learning analyses
ORAL
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Presenters
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Jesse M Hanlan
University of Pennsylvania
Authors
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Jesse M Hanlan
University of Pennsylvania
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Sam J Dillavou
University of Pennsylvania
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Douglas J Durian
University of Pennsylvania
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Variational Computation of the Committor for Reactive Events In and Out of Equilibrium
ORAL
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Presenters
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Aditya N Singh
University of California, Berkeley
Authors
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Aditya N Singh
University of California, Berkeley
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David T Limmer
University of California, Berkeley
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Predicting Microfluidic Droplet Diameters Using Machine Learning
ORAL
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Presenters
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Serena Holte
University of Minnesota Duluth
Authors
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Serena Holte
University of Minnesota Duluth
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Machine Learning for Metamaterial Design
ORAL
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Publication: R. van Mastrigt et al., Phys. Rev. Lett. 2022.
Presenters
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Ryan van Mastrigt
University of Amsterdam
Authors
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Ryan van Mastrigt
University of Amsterdam
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Marjolein Dijkstra
Utrecht University
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Martin van Hecke
AMOLF Amsterdam & Leiden University, AMOLF
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Corentin Coulais
University of Amsterdam
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Decomposing Long-Time Behavior of Dynamical Systems through Linear Regression
ORAL
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Presenters
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Sam Quinn
Georgia Institute of Technology
Authors
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Sam Quinn
Georgia Institute of Technology
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Joshua L. Pughe-Sanford
Georgia Institute of Technology
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Roman O Grigoriev
Georgia Tech
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Learning hydrodynamic equations from microscopic Langevin simulations of self-propelled particles dynamics
ORAL
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Presenters
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Bappaditya Roy
MathAM-OIL
Authors
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Bappaditya Roy
MathAM-OIL
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Natsuhiko Yoshinaga
Tohoku Univ
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Unraveling the role of Hydrogen bonds via two machine learning methods
ORAL
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Presenters
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Dizhou Wu
Wake Forest University
Authors
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Freddie R Salsbury
Wake Forest University
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Dizhou Wu
Wake Forest University
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Statistical properties of empirical cross-covariance matrices of correlated large-dimensional datasets
ORAL
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Publication: N/A
Presenters
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Arabind Swain
Emory University
Authors
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Arabind Swain
Emory University
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Eslam Abdelaleem
Emory University
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Ilya M Nemenman
Emory, Emory University
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Automated neuron tracking using deep learning and targeted augmentation allows fast collection of C. elegans whole brain calcium activity during behavior
ORAL
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Publication: https://www.biorxiv.org/content/10.1101/2022.03.15.484536v1
Presenters
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Core Francisco Park
Harvard University
Authors
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Core Francisco Park
Harvard University
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Sahand Rahi
Ecole Polytechnique Federale de Lausanne
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Aravinthan Samuel
Harvard University
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Mahsa Barzegar Keshteli
Ecole Polytechnique Federale de Lausanne
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Kseniia Korchagina
Ecole Polytechnique Federale de Lausanne
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Ariane Delrocq
Ecole Polytechnique Federale de Lausanne
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Vladislav Susoy
Harvard University
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Corinne Jones
Ecole Polytechnique Federale de Lausanne
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