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17 March 2023 Reconstructing seen and unseen attractors from data via autonomous-mode reservoir computing
André Röhm, Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki, Daniel J. Gauthier, Ingo Fischer, Makoto Naruse
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Proceedings Volume PC12438, AI and Optical Data Sciences IV; PC124380E (2023) https://doi.org/10.1117/12.2648645
Event: SPIE OPTO, 2023, San Francisco, California, United States
Abstract
Reservoir computing is a powerful tool for creating digital twins of a target systems. They can both predict future values of a chaotic timeseries to a high accuracy and also reconstruct the general properties of a chaotic attractor. In this. We show that their ability to learn the dynamics of a complex system can be extended to systems with multiple co-existing attractors, here a four-dimensional extension of the well-known Lorenz chaotic system. Even parts of the phase space that were not in the training set can be explored with the help of a properly-trained reservoir computer. This includes entirely separate attractors, which we call "unseen". Training on a single noisy trajectory is sufficient. Because Reservoir Computers are substrate-agnostic, this allows the creation of conjugate autonomous reservoir computers for any target dynamical systems.
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André Röhm, Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki, Daniel J. Gauthier, Ingo Fischer, and Makoto Naruse "Reconstructing seen and unseen attractors from data via autonomous-mode reservoir computing", Proc. SPIE PC12438, AI and Optical Data Sciences IV, PC124380E (17 March 2023); https://doi.org/10.1117/12.2648645
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KEYWORDS
Computing systems
Complex systems
Dynamical systems
Photonics
Machine learning
Systems modeling
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