Low-Order Modeling and Machine Learning in Fluid Dynamics: Methods III
ORAL · J26 · ID: 3583491
Presentations
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A priori Assessment of Tensor Networks Encoding for Reduced Order Modeling of Isotropic Turbulence
ORAL
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Presenters
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Massen Esmaeili
- University of Pittsburgh
Authors
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Massen Esmaeili
- University of Pittsburgh
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Hirad Alipanah
- University of Pittsburgh
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Robert Pinkston
- University of Pittsburgh
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Peyman Givi
- University of Pittsburgh
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Daniel Livescu
- Los Alamos National Laboratory (LANL)
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Dieter Jaksch
- University of Hamburg
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Juan José Mendoza Arenas
- University of Pittsburgh
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A Machine Learning Augmented RANS Approach for Rapid and Accurate Simulations of Periodic Unsteady Flows
ORAL
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Presenters
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Lean Fang
- Iowa State University
Authors
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Lean Fang
- Iowa State University
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Ping He
- Iowa State University
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Transition prediction in high-speed boundary layers using Bayesian neural operators
ORAL
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Presenters
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Yue Hao
- Johns Hopkins University
Authors
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Yue Hao
- Johns Hopkins University
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Charles Meneveau
- Johns Hopkins University
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Tamer A Zaki
- Johns Hopkins University
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Low-Dimensional Latent Space Representation of the Parametric Dependence of Transient Instability Growth
ORAL
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Presenters
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Balu Nadiga
- Los Alamos National Laboratory
- Los Alamos National Laboratory (LANL)
Authors
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Daniel Messenger
- LANL
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Balu Nadiga
- Los Alamos National Laboratory
- Los Alamos National Laboratory (LANL)
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Marc Klasky
- LANL
- Los Alamos National Laboratory
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Chemical Kinetics Reduction Using a Self-Evolving Neural Network Framework
ORAL
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Presenters
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Sheharyar Nasir
- University of Kansas
Authors
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Sheharyar Nasir
- University of Kansas
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Cheng Huang
- University of Kansas
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Invertible Neural Autoencoder for Low-Order Modeling of Homogeneous Flow Structures
ORAL
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Publication: https://arxiv.org/abs/2505.15329
Presenters
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Anqiao Ouyang
- Mount Boucherie Secondary School
Authors
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Anqiao Ouyang
- Mount Boucherie Secondary School
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Hongyi Ke
- San Diego State University
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Qi Wang
- San Diego State University
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