Low-Order Modeling and Machine Learning in Fluid Dynamics: Methods IV

Presentations

• Combining LES, Machine Learning, and Reduced-Order Models for Predicting Natural Ventilation

  ORAL

  Nov. 24, 2025, 8:00 AM – Nov. 24, 2025, 8:13 AM

  Presenters

  • Nicholas Gregory Bachand

    • Stanford University

  Authors

  • Nicholas Gregory Bachand

    • Stanford University

  • Themistoklis Vargiemezis

    • Stanford University

  • Catherine Gorle

    • Stanford University

  View abstract →

• Separable Conditional Neural Fields for In-Situ Compression of High-Fidelity Spatiotemporal Turbulence Simulation Data

  ORAL

  Nov. 24, 2025, 8:13 AM – Nov. 24, 2025, 8:26 AM

  Presenters

  • Junyi Guo

    • Cornell University

  Authors

  • Junyi Guo

    • Cornell University

  • Xiantao Fan

    • Cornell University

  • Pan Du

    • University of Notre Dame

  • Jiahang Zhou

    • University of Notre Dame

  • Jian-Xun Wang

    • Cornell University

  View abstract →

• SINDy on slow manifolds

  ORAL

  Nov. 24, 2025, 8:26 AM – Nov. 24, 2025, 8:39 AM

  Publication: Delgado-Cano, D., Kracht, E., Fasel, U., & Herrmann, B. (2025). SINDy on slow manifolds. arXiv preprint arXiv:2507.00747.

  Presenters

  • Benjamin Herrmann

    • Pontificia Universidad Católica de Chile

  Authors

  • Diemen Delgado-Cano

    • Universidad de Chile

  • Erick Kracht

    • Universidad de Chile

  • Urban Fasel

    • Imperial College London

  • Benjamin Herrmann

    • Pontificia Universidad Católica de Chile

  View abstract →

• Abstract Withdrawn

  ORAL · Withdrawn

  Nov. 24, 2025, 8:39 AM – Nov. 24, 2025, 8:52 AM

  View abstract →

• Learning local-to-global flow super-resolution with generative AI

  ORAL

  Nov. 24, 2025, 8:52 AM – Nov. 24, 2025, 9:05 AM

  Presenters

  • Siavash Khodakarami

    • Division of Applied Mathematics, Brown University
    • Brown University

  Authors

  • Siavash Khodakarami

    • Division of Applied Mathematics, Brown University
    • Brown University

  • Zhicheng Wang

    • Division of Applied Mathematics, Brown University
    • Brown University

  • Zhen Zhang

    • Brown University
    • Division of Applied Mathematics, Brown University

  • Khemraj Shukla

    • Division of Applied Mathematics, Brown University
    • Division of Applied Mathematics, Brown University, Providence, RI, 02912, USA

  • Anthony Morales

    • Department of Mechanical and Aerospace Engineering, University of Central Florida

  • Sheikh Salauddin

    • Department of Mechanical and Aerospace Engineering, University of Central Florida

  • Kareem Ahmad

    • University of Central Florida
    • Department of Mechanical and Aerospace Engineering, University of Central Florida

  • George Em Karniadakis

    • Division of Applied Mathematics and School of Engineering, Brown University, Providence, RI, 02912, USA

  View abstract →

• Machine Learning-Based Super-Resolution Reconstruction of Turbulent Flow Simulations over Superhydrophobic Surfaces

  ORAL

  Nov. 24, 2025, 9:05 AM – Nov. 24, 2025, 9:18 AM

  Publication: K. Han and J. Seo, "Machine Learning-Based Super-Resolution Reconstruction of Turbulent Flow Simulations over Superhydrophobic Surfaces," Submitted.

  Presenters

  • Kyungyoun Han

    • kyunghee university

  Authors

  • Jongmin Seo

    • Kyung Hee University

  • Kyungyoun Han

    • kyunghee university

  View abstract →

• Differentiable Autoencoding Neural Operators: Interpretable and Integrable Latent Spaces

  ORAL

  Nov. 24, 2025, 9:18 AM – Nov. 24, 2025, 9:31 AM

  Presenters

  • Siva Viknesh

    • University of Utah

  Authors

  • Siva Viknesh

    • University of Utah

  • Amirhossein Arzani

    • University of Utah

  View abstract →

• Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Conditional Gaussian Koopman Network

  ORAL

  Nov. 24, 2025, 9:31 AM – Nov. 24, 2025, 9:44 AM

  Publication: Chen, C., Chen, N., Zhang, Y., & Wu, J.-L. (2025). CGKN: A deep learning framework for modeling complex dynamical systems and efficient data assimilation. Journal of Computational Physics, 532, 113950.
  Chen, C., Wang, Z., Chen, N., & Wu, J. L. (2025). Modeling partially observed nonlinear dynamical systems and efficient data assimilation via discrete-time conditional Gaussian Koopman network. Computer Methods in Applied Mechanics and Engineering, 445, 118189.

  Presenters

  • Jinlong Wu

    • University of Wisconsin - Madison

  Authors

  • Chuanqi Chen

    • University of Wisconsin - Madison

  • Zhongrui Wang

    • University of Wisconsin–Madison

  • Nan Chen

    • University of Wisconsin - Madison

  • Jinlong Wu

    • University of Wisconsin - Madison

  View abstract →

• Machine Learning Aided Flow Field Reconstruction from Sparse and Noisy Particle Measurements

  ORAL

  Nov. 24, 2025, 9:44 AM – Nov. 24, 2025, 9:57 AM

  Presenters

  • Daria Skalitzky

    • University of Michigan

  Authors

  • Xianzhang Xu

    • University of Michigan

  • Daria Skalitzky

    • University of Michigan

  • Krishnan Mahesh

    • University of Michigan
    • University of Minnesota

  View abstract →

• Characterizing Extreme Events in Turbulent Flows through Sensitivity-Based Modal Decomposition

  ORAL

  Nov. 24, 2025, 9:57 AM – Nov. 24, 2025, 10:10 AM

  Presenters

  • Nicholas Zolman

    • University of Washington

  Authors

  • Nicholas Zolman

    • University of Washington

  • Sajeda Mokbel

    • University of Washington

  • Samuel E Otto

    • Cornell University

  • Steven L Brunton

    • University of Washington

  View abstract →

• Differentiable Hybrid Neural-CFD Modeling of Spatiotemporal Dynamics in 3D Wall-Bounded Turbulence

  ORAL

  Nov. 24, 2025, 10:10 AM – Nov. 24, 2025, 10:23 AM

  Presenters

  • Xiantao Fan

    • Cornell University

  Authors

  • Xiantao Fan

    • Cornell University

  • Meet H Parikh

    • Cornell University

  • Yi Liu

    • Cornell University
    • University of Notre Dame

  • Meng Wang

    • University of Notre Dame

  • Jian-Xun Wang

    • Cornell University

  View abstract →