Monday, November 19, 2018
8:00AM - 8:13AM
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F32.00001: From Deep to Physics-Informed Learning of Turbulence: Diagnostics
Michael Chertkov, Oliver Hennigh, Ryan King, Arvind Mohan
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Monday, November 19, 2018
8:13AM - 8:26AM
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F32.00002: Neural Network Powered Adjoint Methods - Gradient Based Shape Optimization with Deep Learning
Dana Lynn Ona Lansigan, Chiyu Max Jiang, Philip S Marcus
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Monday, November 19, 2018
8:26AM - 8:39AM
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F32.00003: Data-driven discretization of PDEs
Yohai Bar-Sinai, Stephan Hoyer, Dmitrii Kochkov, Jason Hickey, Michael Phillip Brenner
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Monday, November 19, 2018
8:39AM - 8:52AM
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F32.00004: Surrogate Modeling of High-Order Physics-Based Fluid Modeling Tools
Nicholas Magina, James Tallman, Robert Zacharias
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Monday, November 19, 2018
8:52AM - 9:05AM
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F32.00005: Bridging simulation and deep learning - convolutional neural networks on unstructured grids
Chiyu Max Jiang, Karthik Kashinath, Philip S Marcus, Mr Prabhat
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Monday, November 19, 2018
9:05AM - 9:18AM
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F32.00006: Physics-Informed Generative Learning to Predict Unresolved Physics in Complex Systems
Jinlong Wu, Yang Zeng, Karthik Kashinath, Adrian Albert, Mr Prabhat, Heng Xiao
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Monday, November 19, 2018
9:18AM - 9:31AM
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F32.00007: A transfer learning approach for data-driven turbulence modeling
Rui Fang, David Sondak, Pavlos Protopapas, Sauro Succi
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Monday, November 19, 2018
9:31AM - 9:44AM
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F32.00008: Machine Learning to Improve RANS Turbulent Kinetic Energy Transport Equation
David S Ching, Andrew J Banko, John K Eaton
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Monday, November 19, 2018
9:44AM - 9:57AM
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F32.00009: Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
Heng Xiao, Jinlong Wu, Jianxun Wang, Eric G Paterson
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Monday, November 19, 2018
9:57AM - 10:10AM
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F32.00010: Interpretability of Machine Learning Models for the Reynolds Stress Tensor in Reynolds-Averaged Navier-Stokes Simulations
Andrew J. Banko, David S. Ching, Julia Ling, John K. Eaton
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