653 citations · 4.7k across the 77 of their papers we have counts for
7 papers · 1 filter
Controlling Chaotic Maps using Next-Generation Reservoir Computing
Robert M. Kent, Wendson A. S. Barbosa, Daniel J. Gauthier
In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dyna…
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…
Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing
Wendson A. S. Barbosa, Daniel J. Gauthier
Forecasting the behavior of high-dimensional dynamical systems using machine learning requires efficient methods to learn the underlying physical model. We demonstrate spatiotempor…
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…
Next Generation Reservoir Computing
Daniel J. Gauthier, Erik Bollt, Aaron Griffith +1
Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it require…
Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
Aaron Griffith, Andrew Pomerance, Daniel J. Gauthier
We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir pe…