Low-Order Modeling and Machine Learning in Fluid Dynamics: Methods I
ORAL · A11 · ID: 3583474
Presentations
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Weak Dominant Balance: A robust method for identifying structure in more complex fluid flows
ORAL
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Presenters
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Samuel Ahnert
- University of Washington
Authors
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Samuel Ahnert
- University of Washington
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Esther Lagemann
- AI Institute in Dynamic Systems, University of Washington
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Jane Bae
- California Institute of Technology
- Caltech
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Ricardo Vinuesa
- KTH Royal Institute of Technology
- University of Michigan
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Christian Lagemann
- University of Washington
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Steven L Brunton
- University of Washington
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Adaptive Low-Rank Tensor Manifolds for Time-Resolved Velocimetry (1): The Algorithm
ORAL
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Presenters
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Hessam Babaee
- University of Pittsburgh
Authors
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Hessam Babaee
- University of Pittsburgh
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Nazmus Sakib
- University of Pittsburgh
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Explainable Data-Driven RANS Closures for Turbulence Modeling
ORAL
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Publication: We are preparing this work as a manuscript to submit it to the journal.
Presenters
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Uma Balakrishnan
- Sandia National Laboratories
Authors
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Uma Balakrishnan
- Sandia National Laboratories
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William Jackson Rider
- Sandia National Laboratories
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Eric Parish
- Sandia National Lab
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Matthew Barone
- Sandia National Laboratories
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Prediction of subgrid interfacial area in two-phase turbulent flows using convolutional neural network-based architectures
ORAL
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Presenters
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Anirban Bhattacharjee
- Georgia Institute of Technology
Authors
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Anirban Bhattacharjee
- Georgia Institute of Technology
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Luis H Hatashita
- Georgia Institute of Technology
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Suhas S Jain
- Georgia Institute of Technology
- Georgia Institute of Technology, Flow Physics and Computational Sciences Lab
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Neural operator-enabled closure for stochastically forced Burgers' equation.
ORAL
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Presenters
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Sotiris Catsoulis
- California Institute of Technology
Authors
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Sotiris Catsoulis
- California Institute of Technology
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George Stepaniants
- California Institute of Technology
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Andrew Stuart
- Caltech
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Tim Colonius
- California Institute of Technology
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oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
ORAL
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Publication: Planned paper: oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
Presenters
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Daniel Dehtyriov
- University of Oxford
Authors
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Daniel Dehtyriov
- University of Oxford
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Jonathan F MacArt
- University of Notre Dame
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Justin Sirignano
- University of Oxford
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Adaptive Low-Rank Tensor Manifolds for Time-Resolved Velocimetry (2): Validation using DNS and Experimental Data
ORAL
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Presenters
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Nazmus Sakib
- University of Pittsburgh
Authors
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Nazmus Sakib
- University of Pittsburgh
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James T Wiswall
- Naval Nuclear Laboratory
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Alexander G Mychkovsky
- Naval Nuclear Laboratory
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Hessam Babaee
- University of Pittsburgh
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