407 citations · 1.4k across the 17 of their papers we have counts for
4 papers · 1 filter
Learning unseen coexisting attractors
Daniel J. Gauthier, Ingo Fischer, André Röhm
Reservoir computing is a machine learning approach that can generate a surrogate model of a dynamical system. It can learn the underlying dynamical system using fewer trainable par…
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks
Mirko Goldmann, Claudio R. Mirasso, Ingo Fischer +1
We design scalable neural networks adapted to translational symmetries in dynamical systems, capable of inferring untrained high-dimensional dynamics for different system sizes. We…
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing
André Röhm, Daniel J. Gauthier, Ingo Fischer
Reservoir computers are powerful tools for chaotic time series prediction. They can be trained to approximate phase space flows and can thus both predict future values to a high ac…
Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops
Florian Stelzer, André Röhm, Raul Vicente +2
Deep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neur…