5 papers
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…
Predicting two-dimensional spatiotemporal chaotic patterns with optimized high-dimensional hybrid reservoir computing
Tamon Nakano, Sebastian Baur, Christoph Räth
As an alternative approach for predicting complex dynamical systems where physics-based models are no longer reliable, reservoir computing (RC) has gained popularity. The hybrid ap…
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…
Controlling nonlinear dynamical systems into arbitrary states using machine learning
Alexander Haluszczynski, Christoph Räth
We propose a novel and fully data driven control scheme which relies on machine learning (ML). Exploiting recently developed ML-based prediction capabilities of complex systems, we…
Predicting high-dimensional heterogeneous time series employing generalized local states
Sebastian Baur, Christoph Räth
We generalize the concept of local states (LS) for the prediction of high-dimensional, potentially mixed chaotic systems. The construction of generalized local states (GLS) relies…