2 papers
cs.LG2022
Actively Learning Costly Reward Functions for Reinforcement Learning
André Eberhard, Houssam Metni, Georg Fahland +2
Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent…
physics.flu-dyn2021
Tripping and laminar--turbulent transition: Implementation in RANS--EVM
N. Tabatabaei, G. Fahland, A. Stroh +5
Fundamental fluid--mechanics studies and many engineering developments are based on tripped cases. Therefore, it is essential for CFD simulations to replicate the same forced trans…