Low-Order Modeling and Machine Learning in Fluid Dynamics: General I
FOCUS · A12 · ID: 2665169
Presentations
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Single-snapshot machine learning for super-resolution analysis of turbulence
ORAL
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Presenters
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Kai Fukami
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles
Authors
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Kai Fukami
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles
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Kunihiko Taira
University of California, Los Angeles, Department of Mechanical and Aerospace Engineering, University of California, Los Angeles
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Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks
ORAL
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Presenters
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Shivam Barwey
Argonne National Laboratory
Authors
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Shivam Barwey
Argonne National Laboratory
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Pinaki Pal
Argonne National Laboratory
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Saumil S Patel
Argonne National Laboratory
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Riccardo Balin
Argonne National Laboratory
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Bethany A Lusch
Argonne National Laboratory
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Venkatram Vishwanath
Argonne National Laboratory
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Romit Maulik
Pennsylvania State University
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RAMESH BALAKRISHNAN
Argonne National Laboratory
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Physics-informed image inpainting for fluid flow reconstruction
ORAL
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Presenters
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Alvaro Moreno Soto
Universidad Carlos III de Madrid
Authors
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Alvaro Moreno Soto
Universidad Carlos III de Madrid
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Manuel Soler
Universidad Carlos III de Madrid
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Stefano Discetti
Department of Aerospace Engineering, Universidad Carlos III de Madrid, Avda. Universidad 30, Legan´es, 28911, Madrid, Spain., Universidad Carlos III de Madrid
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Toward guaranteed stability in low-order data-driven models with closure modeling
ORAL
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Presenters
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Vamsi Krishna Chinta
University of Minnesota
Authors
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Vamsi Krishna Chinta
University of Minnesota
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Diganta Bhattacharjee
University of Minnesota
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Peter Seiler
University of Michigan
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Maziar S Hemati
University of Minnesota
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Predictive reduced order models of tsunamis via neural Galerkin-projection and heirarchical pooling
ORAL
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Presenters
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Shane X Coffing
Los Alamos National Laboratory (LANL)
Authors
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Shane X Coffing
Los Alamos National Laboratory (LANL)
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John Tipton
Los Alamos National Laboratory
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Darren Engwirda
Los Alamos National Laboratory
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Arvind T Mohan
Los Alamos National Laboratory (LANL)
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Accelerating dynamic fluid solvers with pure data-driven deep learning models.
ORAL
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Presenters
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Isaac C Bannerman
Rensselaer Polytechnic Institute
Authors
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Isaac C Bannerman
Rensselaer Polytechnic Institute
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Shaowu Pan
Rensselaer Polytechnic Institute
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Lucy T Zhang
Rensselaer Polytechnic Institute
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Abstract Withdrawn
ORAL Withdrawn
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Bayesian autoencoders for physics learning
ORAL
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Publication: 1. Mars Gao, L., and J. Nathan Kutz. "Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants." Proceedings of the Royal Society A 480.2286 (2024): 20230506.
2. Williams, Jan P., Olivia Zahn, and J. Nathan Kutz. "Sensing with shallow recurrent decoder networks." arXiv preprint arXiv:2301.12011 (2023).Presenters
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Liyao Mars M Gao
University of Washington
Authors
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Liyao Mars M Gao
University of Washington
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J. Nathan Kutz
University of Washington, University of Washington, AI Institute for Dynamic Systems
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A multilevel flow agnostic LES approach using deep learning
ORAL
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Presenters
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Dhawal Buaria
Texas Tech University, USA and MPI-DS, Göttingen, Germany, Texas Tech University, USA
Authors
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Dhawal Buaria
Texas Tech University, USA and MPI-DS, Göttingen, Germany, Texas Tech University, USA
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A hierarchical deep neural-network for long-term prediction of turbulent flow
ORAL
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Presenters
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Jonghyun Chae
Pohang Univ of Sci & Tech
Authors
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Jonghyun Chae
Pohang Univ of Sci & Tech
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Youngmin Jeon
Pohang Univ of Sci & Tech
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Donghyun You
Pohang Univ of Sci & Tech
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