5 citations · 10 across the 5 of their papers we have counts for
4 papers · 1 filter
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially…
A deep learning approach to wall-shear stress quantification: From numerical training to zero-shot experimental application
Esther Lagemann, Julia Roeb, Steven L. Brunton +1
The accurate quantification of wall-shear stress dynamics is of substantial importance for various applications in fundamental and applied research, spanning areas from human healt…
Extending the aircraft flight envelope by mitigating transonic airfoil buffet
Esther Lagemann, Steven L. Brunton, Wolfgang Schröder +1
In the age of globalization, commercial aviation plays a central role in maintaining our international connectivity by providing fast air transport services for passengers and frei…
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements
Esther Lagemann, Steven L. Brunton, Christian Lagemann
Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology,…