Computational Fluid Dynamics: Data-Driven Modeling
ORAL · E31
Presentations
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On the Conditioning of Machine-Learning-Assisted Turbulence Modeling
ORAL
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Authors
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Jinlong Wu
Virginia Tech
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Rui Sun
Virginia Tech
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Qiqi Wang
Massachusetts Institute of Technology, Massachusetts Inst of Tech-MIT, MIT
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Heng Xiao
Virginia Tech
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Data-Driven Augmentations of Second Moment Closures for Turbulent Flow Prediction
ORAL
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Authors
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Walter Crosby
University of Michigan
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Anand Pratap Singh
University of Michigan
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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
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Authors
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Romit Maulik
Oklahoma State University
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Omer San
Oklahoma State University
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Deep Learning Fluid Mechanics
ORAL
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Authors
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Amir Barati Farimani
Stanford University
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Joseph Gomes
Stanford University
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Vijay Pande
Stanford University
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A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks
ORAL
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Authors
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Arvind Mohan
The Ohio State University
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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
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Authors
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Sangseung Lee
Pohang University of Science and Technology
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Donghyun You
Pohang University of Science and Technology
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