3 papers
cs.LG2021
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…
physics.data-an2020
Reducing network size and improving prediction stability of reservoir computing
Alexander Haluszczynski, Jonas Aumeier, Joschka Herteux +1
Reservoir computing is a very promising approach for the prediction of complex nonlinear dynamical systems. Besides capturing the exact short-term trajectories of nonlinear systems…
physics.data-an2019
Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing
Alexander Haluszczynski, Christoph Räth
The prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, reservoir computing turned out to…