MS1-12A Data-driven closure models for RANS and LES

Fri, 28/04/2023
10:00 - 12:00
Auditorium E
Chaired by:
Prof. Heng Xiao ( University of Stuttgart)

Contributions in this session:

  • Learning and Forecasting the Effective Dynamics of Complex Systems across Scales
    P. Vlachas*, G. Arampatzis, C. Uhler, P. Koumoutsakos
  • Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons
    B. List*, L. Chen, N. Thuerey
  • Reinforcement learning-based optimisation of LES closure models
    A. Beck*, M. Kurz
  • Structure-Preserving Machine Learning: Energy-Conserving Neural Network for Turbulence Closure-Modelling
    T. van Gastelen*, B. Sanderse, W. Edeling
  • Towards model-based deep reinforcement learning for accelerated learning from simulations
    A. Weiner*, J. Geise
  • Active flow control on 2D wings and separation in boundary layers through deep reinforcement learning
    F. Alcántara-Ávila*, P. Suárez, A. Miró, J. Rabault, B. Font, O. Lehmkhul, R. Vinuesa