MS1-08D Equation- and Data-driven Reduced-order Modeling for Fluid Flows

Thu, 27/04/2023
10:00 - 12:00
Auditorium K
Chaired by:
Mr. Balint Kaszas (ETH Zurich)

Contributions in this session:

  • Strategies for improving the performance of Physics-Informed Neural Networks for predicting flow fields in bioreactors
    V. Travnikova*, E. von Lieres, M. Behr
  • Model reduction with latent variables for discretely filtered equations
    H. Rosenberger*, B. Sanderse
  • Subspace-Distance-Enabled Active Learning for Non-Intrusive Reduced-Order Modeling of Parametric Fluid Flows
    H. Kapadia*, L. Feng, P. Benner
  • Reduced order modeling with variational multiscale method for environmental flows
    S. Dave*, A. Korobenko
  • A variational Bayesian non-linear reduced order modeling in fluid dynamics
    N. Akkari*, F. Casenave