activity
20232026
most citedUncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements

3 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CE2026

Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure

Samuel Ahnert, Esther Lagemann, H. Jane Bae +4

Extracting interpretable, localized physical mechanisms from complex spatiotemporal data is a foundational challenge across physics, biology, and engineering, but has remained out…

physics.flu-dyn2025

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…

physics.flu-dyn2024

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…

physics.flu-dyn20242 cited

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…

physics.flu-dyn20233 cited

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,…