Minisymposium: Bayesian Inference for Synthesis of Models and Data in Fluid Mechanics

Presentations

• Foundations of Bayesian Inference and Application to Dynamical System Identification

  ORAL · Invited

  Nov. 26, 2024, 8:00 AM – Nov. 26, 2024, 8:26 AM

  Publication: Niven, R.K., Cordier, L., Mohammad-Djafari, A., Abel, M., Quade, M., Dynamical system identification, model selection and model uncertainty quantification by Bayesian inference, arXiv:2401.16943v2.

  Presenters

  • Robert K Niven

    • University of New South Wales

  Authors

  • Robert K Niven

    • University of New South Wales

  • Laurent Cordier

    • Univ de Poitiers

  • Ali Mohammad-Djafari

    • CentraleSupelec, Gif-sur-Yvette, France.

  • Markus Abel

    • Ambrosys GmbH, Potsdam, Germany

  • Markus Quade

    • Ambrosys GmbH, Potsdam, Germany

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• The Elephant in the Room: Adjoint-accelerated Bayesian Inference into multi-parameter CFD

  ORAL · Invited

  Nov. 26, 2024, 8:26 AM – Nov. 26, 2024, 8:52 AM

  Publication: https://arxiv.org/abs/2406.18464
  https://doi.org/10.1109/TIP.2022.3228172
  https://doi.org/10.1017/jfm.2022.503

  Presenters

  • Matthew P Juniper

    • Univ of Cambridge

  Authors

  • Matthew P Juniper

    • Univ of Cambridge

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• Quantification and reduction of RANS model uncertainties through regional Bayesian calibration and model mixtures

  ORAL · Invited

  Nov. 26, 2024, 8:52 AM – Nov. 26, 2024, 9:18 AM

  Presenters

  • Paola Cinnella

    • Sorbonne Université

  Authors

  • Paola Cinnella

    • Sorbonne Université

  • Cécile Roques

    • Sorbonne Université and Safran Tech

  • Grégory Dergham

    • Safran Tech

  • Xavier Merle

    • Ecole Nationale Supérieure d'Arts et Métiers

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• Bayesian-based merging of data assimilation and machine learning to learn unsteady turbulence models from sparse data

  ORAL · Invited

  Nov. 26, 2024, 9:18 AM – Nov. 26, 2024, 9:44 AM

  Publication: Villiers, R., Mons, V., Sipp, D., Lamballais, E., Meldi, M.: Enhancing Unsteady Reynolds-Averaged Navier-Stokes modelling from sparse data through data assimilation and machine learning. To be submitted to Flow, Turbulence and Combustion

  Presenters

  • Vincent Mons

    • DAAA, ONERA, Institut Polytechnique de Paris

  Authors

  • Vincent Mons

    • DAAA, ONERA, Institut Polytechnique de Paris

  • Raphaël Villiers

    • DAAA, ONERA, Institut Polytechnique de Paris

  • Denis Sipp

    • DAAA, ONERA, Institut Polytechnique de Paris
    • Onera

  • Eric Lamballais

    • Curiosity Group, Pprime Institute, CNRS-Univ-Poitiers-ISAE/ENSMA

  • Marcello Meldi

    • Univ. Lille, CNRS, ONERA, Arts et Metiers ParisTech, Centrale Lille, UMR 9014- LMFL- Laboratoire de Mecanique des fluides de Lille

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• Bayesian model selection for the squeeze flow of soft matter

  ORAL · Invited

  Nov. 26, 2024, 9:44 AM – Nov. 26, 2024, 9:57 AM

  Publication: Rinkens, A., Verhoosel, C. V., & Jaensson, N. O. (2023). Uncertainty quantification for the squeeze flow of generalized Newtonian fluids. Journal of Non-Newtonian Fluid Mechanics, 322, 105154

  Presenters

  • Aricia Rinkens

    • Eindhoven University of Technology

  Authors

  • Aricia Rinkens

    • Eindhoven University of Technology

  • Clemens V Verhoosel

    • Eindhoven University of Technology

  • Nick O Jaensson

    • TU Eindhoven

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• Multi-fidelity modeling and uncertainty quantification of heterogeneous roughness

  ORAL · Invited

  Nov. 26, 2024, 9:57 AM – Nov. 26, 2024, 10:10 AM

  Presenters

  • YoungIn Shin

    • Massachusetts Institute of Technology

  Authors

  • YoungIn Shin

    • Massachusetts Institute of Technology

  • Miles J Chan

    • California Institute of Technology
    • Caltech

  • Jianyu Wang

    • Center for Turbulence Research, Stanford University

  • Tony Zahtila

    • Stanford University

  • Catherine Gorle

    • Stanford University

  • Gianluca Iaccarino

    • Stanford University

  • Michael F Howland

    • Massachusetts Institute of Technology

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• Uncertainty Quantification of Separated Flows Using Bayesian Neural Networks

  ORAL · Invited

  Nov. 26, 2024, 10:10 AM – Nov. 26, 2024, 10:23 AM

  Presenters

  • Tyler S Buchanan

    • Delft University of Technology

  Authors

  • Tyler S Buchanan

    • Delft University of Technology

  • Richard P Dwight

    • Delft University of Technology

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• Particle filters and stochastic transport models for geophysical data assimilation: localization and scalability

  ORAL · Invited

  Nov. 26, 2024, 10:23 AM – Nov. 26, 2024, 10:36 AM

  Presenters

  • Eliana Fausti

    • Imperial College London

  Authors

  • Eliana Fausti

    • Imperial College London

  • Dan Crisan

    • Imperial College London

  View abstract →