5 papers
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…
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…
Learning Individual Dynamics from Sparse Cross-Sectional Snapshots
Christian Lagemann, Kai Lagemann, Steven L. Brunton +1
Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires dense longitudinal tracking. Whe…
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Nicholas Zolman, Christian Lagemann, Urban Fasel +2
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in complex environments, such as stabilizing a tokamak f…
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…