Interact: Machine Learning for Fluid Mechanics
ORAL · C36 · ID: 3585744
Presentations
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Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Generative Models
ORAL
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Publication: Dong, X., Yang, H., & Wu, J. L. (2025). Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models. arXiv preprint arXiv:2506.20771.
Presenters
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Xinghao Dong
- University of Wisconsin - Madison
Authors
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Xinghao Dong
- University of Wisconsin - Madison
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Huchen Yang
- University of Wisconsin - Madison
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Jinlong Wu
- University of Wisconsin - Madison
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Extracting time-varying causal modes of aerodynamic flows with information-theoretic machine learning
ORAL
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Publication: Fukami, K., & Araki, R. (2025). Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows. AIAA Journal.
Presenters
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Kai Fukami
- Tohoku University
Authors
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Kai Fukami
- Tohoku University
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Ryo Araki
- Tokyo University of Science
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Space-time model reduction using SPOD modes
ORAL
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Publication: Space-time model reduction in the frequency domain, Frame and Towne, arXiv, 2024
Linear model reduction using SPOD modes, Frame, Lin, Schmidt, Towne, arXiv 2024Presenters
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Peter Keaton Frame
- University of Michigan
Authors
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Peter Keaton Frame
- University of Michigan
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Aaron S. Towne
- University of Michigan
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Tensor Train-based cross interpolation method for solving high-dimensional PDF transport equation of turbulent flows
ORAL
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Presenters
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Behzad Ghahremani
- University of Pittsburgh
Authors
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Behzad Ghahremani
- University of Pittsburgh
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Peyman Givi
- University of Pittsburgh
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Hessam Babaee
- University of Pittsburgh
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No more adjoints: Calibrating chaotic dynamical systems with weak-form learning
ORAL
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Presenters
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Romit Maulik
- Argonne National Laboratory
- The Pennsylvania State University
Authors
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Xuyang Li
- The Pennsylvania State University
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John Harlim
- The Pennsylvania State University
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Romit Maulik
- Argonne National Laboratory
- The Pennsylvania State University
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Super-resolution of turbulence with a 4DVar training algorithm
ORAL
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Publication: Page, J. "Super-resolution of turbulence with dynamics in the loss", Journal of Fluid Mechanics 1002, R3 (2025)
Scherer, M. & Linkmann, M. & Page, J. "State estimation with a combination of 4DVar and super-resolution in body-forced turbulence" (in preparation -- working title)Presenters
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Jacob Page
- University of Edinburgh
Authors
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Jacob Page
- University of Edinburgh
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Markus Weyrauch
- Karlsruhe Institute of Technology
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Moritz F Linkmann
- University of Edinburgh
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Field Inversion Machine Learning for Predicting Time-Resolved Unsteady Flows in Dynamic Stall
ORAL
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Presenters
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Ping He
- Iowa State University
Authors
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Ping He
- Iowa State University
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Zilong Li
- Iowa State University
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Lean Fang
- Iowa State University
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Anupam Sharma
- Iowa State University
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The role of the law of the wall in enabling generalization of data-driven turbulence models
ORAL
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Presenters
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Jiaqi Li
- Pennsylvania State University
Authors
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Jiaqi Li
- Pennsylvania State University
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Xiang I. A. Yang
- Pennsylvania State University
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Robert F Kunz
- Pennsylvania State University
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George P Huang
- Wright State University
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Non-Linear Super-Stencils for RANS turbulence model corrections
ORAL
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Publication: https://doi.org/10.1038/s42005-025-02149-3
Presenters
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Jonas Luther
- Eth Zurich
Authors
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Jonas Luther
- Eth Zurich
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Patrick Jenny
- ETH Zurich
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Equivariant Machine Learning of Sub-Grid Scale Closure Models for Large Eddy Simulation
ORAL
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Presenters
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Ryley McConkey
- Massachusetts Institute of Technology
Authors
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Ryley McConkey
- Massachusetts Institute of Technology
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Julia Balla
- Massachusetts Institute of Technology
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Elyssa F Hofgard
- Massachusetts Institute of Technology
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Tess E Smidt
- Massachusetts Institute of Technology
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FIRST INTERACT DISCUSSION WITH POSTERS
COFFEE_KLATCH
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Symmetry-aware Reynolds-averaged turbulence modeling with equivariant neural networks
ORAL
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Publication: A planned paper is currently in the final stages of preparation.
Presenters
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Aaron Miller
- Harvard University
Authors
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Aaron Miller
- Harvard University
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Sahil Kommalapati
- University of Texas at Austin
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Robert D Moser
- University of Texas at Austin
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Petros Koumoutsakos
- Harvard University
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GenAI meets Turbulence: From Super-resolution to Forecasting and Full Field Reconstruction from Sparse Observations
ORAL
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Presenters
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Vivek Oommen
- Brown University
Authors
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Vivek Oommen
- Brown University
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Aniruddha Bora
- Brown University
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George Em Karniadakis
- Division of Applied Mathematics and School of Engineering, Brown University, Providence, RI, 02912, USA
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Siavash Khodakarami
- Division of Applied Mathematics, Brown University
- Brown University
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Zhicheng Wang
- Division of Applied Mathematics, Brown University
- Brown University
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Machine Learning-Assisted Model Blending for Generalizable Turbulence Corrections
ORAL
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Presenters
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Mourad Oulghelou
- Sorbonne University
- Sorbonne Universite
Authors
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Mourad Oulghelou
- Sorbonne University
- Sorbonne Universite
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Paola Cinnella
- Sorbonne Université
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Xavier Merle
- Ecole Nationale Supérieure d'Arts et Métiers
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Capturing Low-Wavenumber Near-Wall Structures via Conditional Generative Modeling
ORAL
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Publication: M H Parikh, X. Fan, J.-X. Wang, Conditional flow matching for generative modeling of near-wall turbulence with quantified uncertainty, Under review, JFM
Presenters
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Meet H Parikh
- Cornell University
Authors
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Meet H Parikh
- Cornell University
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Xiantao Fan
- Cornell University
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Meng Wang
- University of Notre Dame
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Jian-Xun Wang
- Cornell University
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Reynolds Number Effects in Data-driven Learning of Mori-Zwanzig Memory Operators for Lagrangian Particle Dynamics
ORAL
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Presenters
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Rohini Uma-Vaideswaran
- Georgia Institute of Technology
Authors
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Rohini Uma-Vaideswaran
- Georgia Institute of Technology
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Xander M de Wit
- Eindhoven University of Technology
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Michael Woodward
- Los Alamos National Laboratory (LANL)
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Alessandro Gabbana
- Los Alamos National Laboratory (LANL)
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André Freitas
- Dept. Physics and INFN, University of Rome "Tor Vergata", Information Processing and Communications Laboratory, Télécom Paris, IP Paris
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Pui-Kuen Yeung
- Georgia Institute of Technology
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Daniel Livescu
- Los Alamos National Laboratory (LANL)
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Generative modeling for closure and linearized stability of chaotic dynamical systems
ORAL
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Publication: Williams, E., and Darmofal, D., "Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems", arXiv:2504.09750, April 2025.
Presenters
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Emily Williams
- Massachusetts Institute of Technology
Authors
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Emily Williams
- Massachusetts Institute of Technology
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David Darmofal
- Massachusetts Institute of Technology
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Abstract Withdrawn
ORAL · Withdrawn
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HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
ORAL
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Presenters
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Steven L Brunton
- University of Washington
Authors
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Christian Lagemann
- University of Washington
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Jared Callaham
- University of Washington
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Ludger Paehler
- Tech Univ Muenchen
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Sajeda Mokbel
- University of Washington
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Samuel Ahnert
- University of Washington
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Kai Lagemann
- Statistics and Machine Learning, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
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Miro Gondrum
- RWTH Aachen
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Mario Ruettgers
- Pohang Univ of Sci & Tech
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Matthias Meinke
- Institue of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University
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Nikolaus A Adams
- Tech Univ Muenchen
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Esther Lagemann
- AI Institute in Dynamic Systems, University of Washington
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Steven L Brunton
- University of Washington
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Reinforcement Learning for Collaborative Wind Farm Control and Power Optimization
ORAL
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Publication: Mole, A., Weissenbacher, M., Rigas, G., & Laizet, S. (2025). Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control. arXiv preprint arXiv:2506.20554.
Presenters
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Sylvain Laizet
- Imperial College London
Authors
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Sylvain Laizet
- Imperial College London
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Andrew Mole
- Imperial College London
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Max Weissenbacher
- Imperial College London
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Georgios Rigas
- Imperial College London
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SECOND INTERACT DISCUSSION WITH POSTERS
COFFEE_KLATCH
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