1 code implementation • 18 Nov 2019 • Yuanzhao Zhang, Zachary G. Nicolaou, Joseph D. Hart, Rajarshi Roy, Adilson E. Motter
We report on a new type of chimera state that attracts almost all initial conditions and exhibits power-law switching behavior in networks of coupled oscillators.
Disordered Systems and Neural Networks Dynamical Systems Adaptation and Self-Organizing Systems Chaotic Dynamics Pattern Formation and Solitons
1 code implementation • 8 Feb 2019 • Joseph D. Hart, Yuanzhao Zhang, Rajarshi Roy, Adilson E. Motter
Symmetries are ubiquitous in network systems and have profound impacts on the observable dynamics.
Adaptation and Self-Organizing Systems Disordered Systems and Neural Networks Chaotic Dynamics Pattern Formation and Solitons
no code implementations • 29 Oct 2020 • Amitava Banerjee, Joseph D. Hart, Rajarshi Roy, Edward Ott
To achieve this, we first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network.
no code implementations • 3 May 2022 • Thomas L. Carroll, Joseph D. Hart
Additionally, the need to create and connect large numbers of nonlinear nodes makes it difficult to design and build analog reservoir computers that can be faster and consume less power than digital reservoir computers.
no code implementations • 29 Nov 2022 • Joseph D. Hart, Francesco Sorrentino, Thomas L. Carroll
Reservoir computing, a recurrent neural network paradigm in which only the output layer is trained, has demonstrated remarkable performance on tasks such as prediction and control of nonlinear systems.
no code implementations • 28 Feb 2023 • Chad Nathe, Chandra Pappu, Nicholas A. Mecholsky, Joseph D. Hart, Thomas Carroll, Francesco Sorrentino
For all the cases we examined, we found that a good remedy to noise is to low-pass filter the input and the training/testing signals; this typically preserves the performance of the reservoir, while reducing the undesired effects of noise.
no code implementations • 30 Dec 2023 • Joseph D. Hart
Reservoir computing is a machine learning framework that has been shown to be able to replicate the chaotic attractor, including the fractal dimension and the entire Lyapunov spectrum, of the dynamical system on which it is trained.