Description
Reinforcement Learning (RL) is increasingly used to make intelligent decisions in complex computational systems, ranging from fluid mechanics and large-scale scientific simulations to optimisation, robotics, control, healthcare, and autonomous systems. This minisymposium focuses on applied and scalable RL approaches that interact directly with simulations, models, and high-performance computing (HPC) environments.
The session will feature talks on RL at scale including applications such as robotics, flow control, and data-driven modelling in fluid mechanics. The aim is to bring together AI researchers and computational scientists to explore how RL can be embedded within scientific workflows to improve efficiency, robustness, and physical fidelity.