Bulletin of the American Physical Society

72nd Annual Meeting of the APS Division of Fluid Dynamics

Volume 64, Number 13

Saturday–Tuesday, November 23–26, 2019; Seattle, Washington

Session Index

Session C17: Focus Session: Recent Advances in Data-driven and Machine Learning Methods for Turbulent Flows I	Show Abstracts
Chair: Karthik Duraisamy, University of Michigan Room: 4c4

Sunday, November 24, 2019 8:00AM - 8:13AM		C17.00001: Unsteady Flow Field Predictions Using Multi-level Deep Convolutional Autoencoder Networks Jiayang Xu, Karthik Duraisamy Preview Abstract
Sunday, November 24, 2019 8:13AM - 8:26AM		C17.00002: Physics-Constrained Convolutional LSTM Neural Networks for Generative Modeling of Turbulence Arvind Mohan, Daniel Livescu, Michael Chertkov Preview Abstract
Sunday, November 24, 2019 8:26AM - 8:39AM		C17.00003: Physics Informed Learning of Lagrangian Turbulence: Velocity Gradient Tensor over Inertial-Range Geometry Yifeng Tian, Daniel Livescu, Michael Chertkov Preview Abstract
Sunday, November 24, 2019 8:39AM - 8:52AM		C17.00004: Prediction of Aerodynamic Flow Fields Using Spectral Convolutions on Graph Networks James Duvall, Karthik Duraisamy, Yaser Afshar Preview Abstract
Sunday, November 24, 2019 8:52AM - 9:05AM		C17.00005: Potential of using deep neural networks for turbulent-flow predictions Ricardo Vinuesa, Prem A. Srinivasan, Luca Guastoni, Hossein Azizpour, Philipp Schlatter Preview Abstract
Sunday, November 24, 2019 9:05AM - 9:18AM		C17.00006: Turbulence inflow generation using generative adversarial network Junhyuk Kim, Changhoon Lee Preview Abstract
Sunday, November 24, 2019 9:18AM - 9:31AM		C17.00007: Physics-informed Spatio-temporal Deep Learning Models Karthik Kashinath, Adrian Albert, Rui Wang, Mustafa Mustafa, Rose Yu Preview Abstract
Sunday, November 24, 2019 9:31AM - 9:44AM		C17.00008: Neural Network Optimization Under Partial Differential Equation Constraints Karthik Kashinath, Chiyu Jiang, Gavin Eli Jergensen, Mr Prabhat, Philip Marcus Preview Abstract
Sunday, November 24, 2019 9:44AM - 9:57AM		C17.00009: Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM. Pedram Hassanzadeh, Ashesh Chattopadhyay, Krishna Palem, Devika Subramanian Preview Abstract
Sunday, November 24, 2019 9:57AM - 10:10AM		C17.00010: Data-driven super-parametrization using deep learning for large scale turbulent flow in weather/climate modeling Ashesh Chattopadhyay, Adam Subel, Pedram Hassanzadeh, Krishna Palem Preview Abstract

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Bulletin of the American Physical Society

72nd Annual Meeting of the APS Division of Fluid Dynamics

Volume 64, Number 13

Saturday–Tuesday, November 23–26, 2019; Seattle, Washington

Session Index

Session C17: Focus Session: Recent Advances in Data-driven and Machine Learning Methods for Turbulent Flows I

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