Low-Order Modeling and Machine Learning in Fluid Dynamics: Design

ORAL · J11 · ID: 3583219






Presentations

  • ORAL

    Publication: Yang, H., Dong, X., & Wu, J.-L. (2025). Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approach. arXiv preprint arXiv:2504.20319.

    Presenters

    • Huchen Yang

      • University of Wisconsin - Madison

    Authors

    • Huchen Yang

      • University of Wisconsin - Madison
    • Xinghao Dong

      • University of Wisconsin - Madison
    • Jinlong Wu

      • University of Wisconsin - Madison

    View abstract →

  • ORAL

    Publication: Arretche, Ignacio, et al. "High-throughput viscometry via machine-learning from videos of inverted vials." arXiv preprint arXiv:2506.02034 (2025).

    Presenters

    • Ramdas Tiwari

      • University of Illinois at Urbana-Champaign

    Authors

    • Ignacio Arretche

      • University of Illinois Urbana-Champaign
    • Randy H Ewoldt

      • University of Illinois Urbana-Champaign
      • University of Illinois at Urbana-Champaign
    • Sameh H Tawfick

      • University of Illinois Urbana-Champaign
    • Mohammad Tanver Hossain

      • University of Illinois Urbana-Champaign
      • University of Illinois at Urbana-Champaign
    • Ramdas Tiwari

      • University of Illinois at Urbana-Champaign
    • Jacob J Lessard

      • Department of Chemistry, University of Utah
      • University of Utah
    • Mya G Mills

      • University of Illinois Urbana-Champaign
    • Abbie J Kim

      • University of Illinois Urbana-Champaign
    • Connor D Armstrong

      • University of Illinois Urbana-Champaign
    • Kevin Mimini

      • University of Illinois Urbana-Champaign

    View abstract →