1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers
Kai Lagemann, Christian Lagemann, Sach Mukherjee
We propose a method for learning dynamical systems from high-dimensional empirical data that combines variational autoencoders and (spatio-)temporal attention within a framework de…
Deep Learning of Causal Structures in High Dimensions
Kai Lagemann, Christian Lagemann, Bernd Taschler +1
Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particula…