Low-Order Modeling and Machine Learning in Fluid Dynamics: General I
ORAL · K11 · ID: 3583263
Presentations
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Reduced-order modeling of reactive flows interacting with uncertain porous media using a hybrid physics-based and data-driven approach
ORAL
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Publication: Paper in preparation: Reduced-order modeling of reactive flows interacting with uncertain porous media using a hybrid physics-based and data-driven approach: Application to ablative thermal protection systems
Presenters
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Diba Behnoudfar
Oregon State University
Authors
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Diba Behnoudfar
Oregon State University
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Kyle Niemeyer
Oregon State University
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A theoretical eigenanalysis framework for neural autoregressive models of multi-scale chaotic dynamics
ORAL
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Presenters
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Ashesh K Chattopadhyay
University of California, Santa Cruz
Authors
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Ashesh K Chattopadhyay
University of California, Santa Cruz
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Conrad S Ainslie
University of California, Santa Cruz
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Pedram Hassanzadeh
University of Chicago
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Michael Mahoney
UC Berkeley
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Active-SINDy: Intelligent sampling for model discovery in the ultra-low-data limit
ORAL
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Presenters
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Ana Larranaga Janeiro
University of Washington
Authors
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Ana Larranaga Janeiro
University of Washington
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Urban Fasel
Imperial College London
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Steven L Brunton
University of Washington
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Comparing and Computing Invariant Manifolds Used in the Dimensionality Reduction of Dissipative Flows
ORAL
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Presenters
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Gregory Robert Macchio
Princeton University
Authors
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Gregory Robert Macchio
Princeton University
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Clancy W Rowley
Princeton, Princeton University
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A Liquid-Fueled Reactor Network for NOx Prediction in Gas Turbine Combustors
ORAL
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Presenters
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Philip O John
Louisiana State Univerity
Authors
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Philip O John
Louisiana State Univerity
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Opeoluwa Owoyele
Louisiana State University
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Cluster-Based Reduced-Order Modeling of Flat Plate Hydrodynamics Near an Air-Water Interface
ORAL
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Presenters
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Mostafa Khazaee Kuhpar
University of Massachusetts Dartmouth
Authors
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Mostafa Khazaee Kuhpar
University of Massachusetts Dartmouth
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Hadi Samsam-Khayani
West Virginia Iniversity, West Virginia University
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Banafsheh Seyed-Aghazadeh
University of Massachusetts Dartmouth
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Data-driven multi-oscillator-based modeling of unsteady flows
ORAL
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Presenters
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Youngjae Kim
University of California, Los Angeles
Authors
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Youngjae Kim
University of California, Los Angeles
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Koichiro Yawata
Institute of Science Tokyo
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Hiroya Nakao
Institute of Science Tokyo
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Kunihiko Taira
University of California, Los Angeles
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Neural Radiance Fields for tomographic reconstruction in molecular tagging velocimetry
ORAL
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Presenters
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Sandra H Halder
Auburn University
Authors
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Sandra H Halder
Auburn University
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Peter D Huck
Lawrence Livermore National Laboratory
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Mark J Yamakaitis
George Washington University
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Charles Fort
George Washington University
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Bibek Sapkota
Auburn University
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Philippe Matthieu Bardet
George Washington University
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Brian S Thurow
Auburn University
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Surrogate Modeling of Irregular Particle Heating in Gas-Solid Flows Using Deep Operator Networks
ORAL
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Presenters
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Akhil Marayikkottu Vijayan
National Energy Technology Laboratory
Authors
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Akhil Marayikkottu Vijayan
National Energy Technology Laboratory
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Jean F Dietiker
National Energy Technology Laboratory
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Reduced order model for chemistry in high-speed flow simulations using Fourier Neural Operators
ORAL
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Presenters
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Federico Rios Tascon
Stanford University
Authors
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Federico Rios Tascon
Stanford University
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Ryan F Johnson
U.S. Naval Research Laboratory
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Diego D Ortiz
Stanford University
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Peter J Schmid
King Abdullah University of Science and Technology
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Beverley J McKeon
Stanford University
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Physics-Informed Neural Networks for Predicting Steady Incompressible Flow Around Obstacles in Urban and Aerodynamic Settings
ORAL
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Presenters
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Sweety Sarker
Embry-Riddle Aeronautical Univ, Daytona, Florida, 32111, USA
Authors
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Sweety Sarker
Embry-Riddle Aeronautical Univ, Daytona, Florida, 32111, USA
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Brendon A Cavainolo
Embry-Riddle Aeronautical University, Daytona Beach
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Michael Kinzel
Embry Riddle Aeronautical University, Daytona Beach, FL, USA
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Bridging the gap: the interface of experimental and computational data in deep learning for fluid mechanics
ORAL
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Presenters
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Peter Ian James Renn
Caltech
Authors
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Peter Ian James Renn
Caltech
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Morteza Gharib
Caltech
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Deep reinforcement learning control unlocks enhanced heat transfer in turbulent convection
ORAL
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Presenters
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Xiaojue Zhu
Max Planck Institute for Solar System Research
Authors
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Xiaojue Zhu
Max Planck Institute for Solar System Research
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Zisong Zhou
Max Planck Institute for Solar System Research
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