Computational Fluid Dynamics: Data-Driven Modeling

Presentations

• On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

  ORAL

  Nov. 19, 2017, 4:55 PM – Nov. 19, 2017, 5:08 PM

  Authors

  • Jinlong Wu

    • Virginia Tech

  • Rui Sun

    • Virginia Tech

  • Qiqi Wang

    • Massachusetts Institute of Technology
    • Massachusetts Inst of Tech-MIT
    • MIT

  • Heng Xiao

    • Virginia Tech

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• Data-Driven Augmentations of Second Moment Closures for Turbulent Flow Prediction

  ORAL

  Nov. 19, 2017, 5:08 PM – Nov. 19, 2017, 5:21 PM

  Authors

  • Walter Crosby

    • University of Michigan

  • Anand Pratap Singh

    • University of Michigan

  • Karthik Duraisamy

    • Univesity of Michigan Ann arbor
    • University of Michigan

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• A neural network approach for the blind deconvolution of turbulent flows

  ORAL

  Nov. 19, 2017, 5:21 PM – Nov. 19, 2017, 5:34 PM

  Authors

  • Romit Maulik

    • Oklahoma State University

  • Omer San

    • Oklahoma State University

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• Deep Learning Fluid Mechanics

  ORAL

  Nov. 19, 2017, 5:34 PM – Nov. 19, 2017, 5:47 PM

  Authors

  • Amir Barati Farimani

    • Stanford University

  • Joseph Gomes

    • Stanford University

  • Vijay Pande

    • Stanford University

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• A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks

  ORAL

  Nov. 19, 2017, 5:47 PM – Nov. 19, 2017, 6:00 PM

  Authors

  • Arvind Mohan

    • The Ohio State University

  • Datta Gaitonde

    • Ohio State Univ - Columbus
    • Ohio State University
    • The Ohio State University

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• Deep learning of unsteady laminar flow over a cylinder

  ORAL

  Nov. 19, 2017, 6:00 PM – Nov. 19, 2017, 6:13 PM

  Authors

  • Sangseung Lee

    • Pohang University of Science and Technology

  • Donghyun You

    • Pohang University of Science and Technology

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