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