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Using a reservoir computer to learn chaotic attractors, with
  applications to chaos synchronisation and cryptography
v1v2 (latest)

Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronisation and cryptography

8 February 2018
P. Antonik
Marvyn Gulina
J. Pauwels
Serge Massar
ArXiv (abs)PDFHTML

Papers citing "Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronisation and cryptography"

15 / 15 papers shown
Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
Mohammad Shah Alam
William Ott
Ilya Timofeyev
151
0
0
30 May 2025
Characterizing Learning in Spiking Neural Networks with Astrocyte-Like Units
Christopher S. Yang
Sylvester J. Gates III
Dulara De Zoysa
Jaehoon Choe
Wolfgang Losert
Corey B. Hart
136
1
0
09 Mar 2025
Stochastic Reservoir Computers
Stochastic Reservoir Computers
Peter J. Ehlers
H. Nurdin
Daniel Soh
308
10
0
20 May 2024
Efficient Optimisation of Physical Reservoir Computers using only a
  Delayed Input
Efficient Optimisation of Physical Reservoir Computers using only a Delayed InputCommunications Engineer (CE), 2024
Enrico Picco
L. Jaurigue
Kathy Lüdge
Serge Massar
283
5
0
25 Jan 2024
Catch-22s of reservoir computing
Catch-22s of reservoir computingPhysical Review Research (Phys. Rev. Res.), 2022
Yuanzhao Zhang
Sean P. Cornelius
290
19
0
18 Oct 2022
Using Machine Learning to Anticipate Tipping Points and Extrapolate to
  Post-Tipping Dynamics of Non-Stationary Dynamical Systems
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical SystemsChaos (Chaos), 2022
Dhruvit Patel
Edward Ott
237
50
0
01 Jul 2022
Asymptotic Stability in Reservoir Computing
Asymptotic Stability in Reservoir ComputingIEEE International Joint Conference on Neural Network (IJCNN), 2022
Jonathan Dong
Erik Börve
M. Rafayelyan
M. Unser
140
8
0
07 Jun 2022
Interpretable Design of Reservoir Computing Networks using Realization
  Theory
Interpretable Design of Reservoir Computing Networks using Realization Theory
Wei Miao
Vignesh Narayanan
Jr-Shin Li
179
6
0
13 Dec 2021
Learn one size to infer all: Exploiting translational symmetries in
  delay-dynamical and spatio-temporal systems using scalable neural networks
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks
Mirko Goldmann
C. Mirasso
Ingo Fischer
Miguel C. Soriano
AI4CE
256
10
0
05 Nov 2021
Reservoir Computers Modal Decomposition and Optimization
Reservoir Computers Modal Decomposition and Optimization
Chad Nathe
Enrico Del Frate
Thomas L. Carroll
L. Pecora
A. Shirin
F. Sorrentino
72
1
0
13 Jan 2021
Reservoir Computing meets Recurrent Kernels and Structured Transforms
Reservoir Computing meets Recurrent Kernels and Structured TransformsNeural Information Processing Systems (NeurIPS), 2020
Jonathan Dong
Ruben Ohana
M. Rafayelyan
Florent Krzakala
TPM
195
22
0
12 Jun 2020
Dimension of Reservoir Computers
Dimension of Reservoir ComputersChaos (Chaos), 2019
T. Carroll
71
20
0
10 Dec 2019
Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation
Data-driven Reconstruction of Nonlinear Dynamics from Sparse ObservationJournal of Computational Physics (JCP), 2019
K. Yeo
93
30
0
10 Jun 2019
Dynamical Component Analysis (DyCA): Dimensionality Reduction For
  High-Dimensional Deterministic Time-Series
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series
B. Seifert
K. Korn
Steffen Hartmann
C. Uhl
98
15
0
26 Jul 2018
Machine-learning inference of fluid variables from data using reservoir
  computing
Machine-learning inference of fluid variables from data using reservoir computing
Kengo Nakai
Yoshitaka Saiki
AI4CE
207
66
0
23 May 2018
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