Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing
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[1] Michelle Girvan,et al. Encoding of a Chaotic Attractor in a Reservoir Computer: A Directional Fiber Investigation , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[2] George J. Pappas,et al. Teaching recurrent neural networks to infer global temporal structure from local examples , 2021, Nature Machine Intelligence.
[3] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[4] Christoph Räth,et al. Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing. , 2019, Chaos.
[5] Erik Bollt,et al. Next generation reservoir computing , 2021, Nature Communications.
[6] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[7] Herbert Jaeger,et al. The''echo state''approach to analysing and training recurrent neural networks , 2001 .
[8] Edward Ott,et al. Attractor reconstruction by machine learning. , 2018, Chaos.
[9] Julien Clinton Sprott,et al. Coexisting Hidden Attractors in a 4-D Simplified Lorenz System , 2014, Int. J. Bifurc. Chaos.
[10] M. C. Soriano,et al. Constructive Role of Noise for High-Quality Replication of Chaotic Attractor Dynamics Using a Hardware-Based Reservoir Computer , 2019, Physical Review Applied.
[11] S. Billings. Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains , 2013 .
[12] Jaideep Pathak,et al. Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. , 2017, Chaos.
[13] Tongfeng Weng,et al. Mapping topological characteristics of dynamical systems into neural networks: A reservoir computing approach. , 2020, Physical review. E.
[14] Joschka Herteux,et al. Reducing network size and improving prediction stability of reservoir computing. , 2020, Chaos.
[15] Harald Haas,et al. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.
[16] Kailiang Wu,et al. Data Driven Governing Equations Approximation Using Deep Neural Networks , 2018, J. Comput. Phys..
[17] Huanfei Ma,et al. Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing. , 2019, Chaos.
[18] Gao Zhi-zhong. A Novel Hyperchaotic System , 2011 .
[19] Juan-Pablo Ortega,et al. Reservoir Computing Universality With Stochastic Inputs , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[20] Guang-Bin Huang,et al. Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[21] Conrad Sanderson,et al. Armadillo: a template-based C++ library for linear algebra , 2016, J. Open Source Softw..