3 papers
cs.LG2026
Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems
Jonas Braun, Fabian Fischbach, Daniel Köglmayr +2
Machine learning methods predict many real-world systems with remarkable accuracy, but they are typically treated as black boxes that offer no insight into which interactions drive…
nlin.CD2026
Controlling Dynamical Systems into Unseen Target States Using Machine Learning
Daniel Köglmayr, Alexander Haluszczynski, Christoph Räth
We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly diffe…
nlin.CD2026
Two-shot learning of multiple strange attractors
Daniel Köglmayr, Miralem Spahic, Andrew Flynn +1
The brain combines short- and long-term memory to process, store, and recall multiple different pieces of information. Inspired by this and recent results on multifunctional and pa…