| Friday, March 19, 2021 8:00AM - 8:36AM
 Live
 
 |  | X05.00001: Calculating the entropy of physical systems with Machine Learning Invited Speaker: 
Yohai Bar-Sinai
 
 
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| Friday, March 19, 2021 8:36AM - 8:48AM
 Live
 
 |  | X05.00002: Predicting Erosion Channel First Passage with Machine Learning Isaac Khor, Li Han, Arshad Kudrolli
 
 
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| Friday, March 19, 2021 8:48AM - 9:00AM
 Live
 
 |  | X05.00003: Machine Learning Prediction of Avalanche-like Events in Knitted Fabric Adèle Douin, Frederic Lechenault, Jean-Philippe Bruneton
 
 
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| Friday, March 19, 2021 9:00AM - 9:12AM
 Live
 
 |  | X05.00004: What makes a clog: characterizing 2D granular hopper flows using machine learning methods Jesse Hanlan, Douglas J Durian
 
 
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| Friday, March 19, 2021 9:12AM - 9:24AM
 Live
 
 |  | X05.00005: Predicting Plasticity in 3D Model Glasses Using the Local Yield Stress Method Dihui Ruan, Sylvain Patinet, Michael Falk
 
 
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| Friday, March 19, 2021 9:24AM - 9:36AM
 Live
 
 |  | X05.00006: Predicting nonlinear stochastic and quantum dynamics without PDEs Alasdair Hastewell, Jorn Dunkel
 
 
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| Friday, March 19, 2021 9:36AM - 9:48AM
 Live
 
 |  | X05.00007: Soft Matter Physics for Machine Learning: Dynamical loss functions Miguel Ruiz Garcia, Ge Zhang, Sam Schoenholz, Andrea Liu
 
 
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| Friday, March 19, 2021 9:48AM - 10:00AM
 Live
 
 |  | X05.00008: Large-scale visualization with machine learning of dislocation networks in colloidal single crystals Ilya Svetlizky, Seongsoo Kim, Seong Ho Pahng, Agnese Curatolo, Michael Brenner, David Weitz, Frans A Spaepen
 
 
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| Friday, March 19, 2021 10:00AM - 10:12AM
 Live
 
 |  | X05.00009: Statistical properties of ridge networks in crumpled sheets Catalin Veghes CVeghes@clarku.edu, Li Han, Arshad Kudrolli
 
 
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| Friday, March 19, 2021 10:12AM - 10:24AM
 Live
 
 |  | X05.00010: Machine Learning of Mechanisms in Combinatorial Metamaterials Ryan van Mastrigt, Corentin Coulais, Martin Van Hecke, Marjolein Dijkstra
 
 
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| Friday, March 19, 2021 10:24AM - 10:36AM
 On Demand
 
 |  | X05.00011: Simplifying Physics Informed Neural Networks in case of periodicity to address low quality and sparse data while solving differential equations : an application in fluid dynamics. Gaétan Raynaud, Frederick P. Gosselin, Sébastien Houde
 
 
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