Christian Lagemann

According to our database, Christian Lagemann authored at least 11 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of five.

2026

Learning Individual Dynamics from Sparse Cross-Sectional Snapshots.

Christian Lagemann, Kai Lagemann, Steven L. Brunton, Sach Mukherjee

CoRR, May, 2026

2025

HydroGym: A Reinforcement Learning Platform for Fluid Dynamics.

Christian Lagemann, Sajeda Mokbel, Miro Gondrum, Mario Rüttgers, Jared Callaham, Ludger Paehler, Samuel Ahnert, Nicholas Zolman, Kai Lagemann, Nikolaus A. Adams, Matthias Meinke, Wolfgang Schröder, Jean-Christophe Loiseau, Esther Lagemann, Steven L. Brunton

CoRR, December, 2025

HydroGym: A Reinforcement Learning Platform for Fluid Dynamics.

Christian Lagemann, Ludger Paehler, Jared Callaham, Sajeda Mokbel, Samuel Ahnert, Kai Lagemann, Esther Lagemann, Nikolaus A. Adams, Steven L. Brunton

Proceedings of the 7th Annual Learning for Dynamics & Control Conference, 2025

Controlling chaotic energy events in fluids with reinforcement learning.

Sajeda Mokbel, Christian Lagemann, Esther Lagemann, Steven L. Brunton

Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems, 2025

2024

A deep learning approach to wall-shear stress quantification: From numerical training to zero-shot experimental application.

Esther Lagemann, Julia Roeb, Steven L. Brunton, Christian Lagemann

CoRR, 2024

Invariance-based Learning of Latent Dynamics.

Kai Lagemann, Christian Lagemann, Sach Mukherjee

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Deep learning of causal structures in high dimensions under data limitations.

Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee

Nat. Mac. Intell., October, 2023

Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements.

Esther Lagemann, Steven L. Brunton, Christian Lagemann

CoRR, 2023

Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers.

Kai Lagemann, Christian Lagemann, Sach Mukherjee

CoRR, 2023

2022

Deep Learning of Causal Structures in High Dimensions.

Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee

CoRR, 2022

2021

Deep recurrent optical flow learning for particle image velocimetry data.

Christian Lagemann, Kai Lagemann, Sach Mukherjee, Wolfgang Schröder

Nat. Mach. Intell., 2021