653 citations · 4.7k across the 77 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…
Mathematical Model of Strong Physically Unclonable Functions Based on Hybrid Boolean Networks
Noeloikeau Charlot, Daniel J. Gauthier, Daniel Canaday +1
We introduce a mathematical framework for simulating Hybrid Boolean Network (HBN) Physically Unclonable Functions (PUFs, HBN-PUFs). We verify that the model is able to reproduce th…
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…
Sensitivity of a Chaotic Logic Gate
Noeloikeau Charlot, Daniel J. Gauthier
Chaotic logic gates or `chaogates' are a promising mixed-signal approach to designing universal computers. However, chaotic systems are exponentially sensitive to small perturbatio…