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