Sunday, November 24, 2019
3:48PM - 4:01PM
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G16.00001: Towards Generalizable Data-driven Turbulence Model Augmentations
Vishal Srivastava, Karthik Duraisamy
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Sunday, November 24, 2019
4:01PM - 4:14PM
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G16.00002: A data-driven approach to modeling turbulent decay at non-asymptotic Reynolds numbers
Mateus Dias Ribeiro, Gavin D Portwood, Peetak Mitra, Tan Mihn Nyugen, Balasubramanya T Nadiga, Michael Chertkov, Anima Anandkumar, David P Schmidt
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Sunday, November 24, 2019
4:14PM - 4:27PM
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G16.00003: A data-driven approach to modeling turbulent flows in an engine environment
Peetak Mitra, Mateus Dias Ribeiro, David Schmidt
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Sunday, November 24, 2019
4:27PM - 4:40PM
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G16.00004: Toward data-driven stochastically forced turbulence closure models
Armin Zare, Anubhav Dwivedi, Mihailo Jovanovic
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Sunday, November 24, 2019
4:40PM - 4:53PM
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G16.00005: A data-driven approach to simulate turbulent bubbly flows using machine learning for modeling bubble size.
Hokyo Jung, Youngjae Kim, Serin Yoon, Gangwoo Ha, Jun Ho Lee, Hyungmin Park, Dongjoo Kim, Jungwoo Kim, Seongwon Kang
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Sunday, November 24, 2019
4:53PM - 5:06PM
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G16.00006: Deep Neural Networks for Data-Driven Turbulence Models
Andrea Beck, David Flad, Claus-Dieter Munz
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Sunday, November 24, 2019
5:06PM - 5:19PM
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G16.00007: Generalized Non-Linear Eddy Viscosity Models for Data-Assisted Reynolds Stress Closure
Basu Parmar, Eric Peters, Kenneth Jansen, Alireza Doostan, John Evans
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Sunday, November 24, 2019
5:19PM - 5:32PM
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G16.00008: An S-frame Discrepancy Correction for Data-Driven Reynolds Stress Closure
Aviral Prakash, Eric Peters, Riccardo Balin, Kenneth Jansen, Alireza Doostan, John Evans
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