MS1-03B Data-Driven Modeling and Machine Learning for Multiphysics Simulations

Wed, 26/04/2023
13:45 - 15:45
Auditorium C
Chaired by:
Dr. Gianmarco Mengaldo (National University of Singapore)

Contributions in this session:

  • (Keynote) Deep Learning-based Reduced Order Modeling for Unsteady Flow and Fluid-Structure Interaction
    R. Jaiman*, R. Gao
  • Assessment of Convolutional Recurrent Autoencoder Network for Learning Wave Propagation
    W. Mallik*, R. Jaiman, J. Jelovica
  • Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics
    M. Lino, S. Fotiadis, A. Bharath, C. Cantwell*
  • Data-driven spectral modeling for the (thermal) quasi-geostrophic equations
    S. Ephrati*, E. Luesink, P. Cifani, A. Franken, B. Geurts
  • Graph Neural Networks to Learn Mesh-Based Fluid Simulations with Physical Symmetries
    M. Horie*, N. Mitsume