Attractor reconstruction by machine learning.
By Zhixin Lu, Brian R. Hunt, Edward Ott. Chaos, 2018
403 citations recorded by the source corpus.
A machine-learning approach called "reservoir computing" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes conditions under which reservoir computing can create an empirical model capable of skillful short-term forecasts and accurate long-term ergodic behavior. We illustrate this theory through numerical experiments. We also argue that the theory applies to certain other machine learning methods for time series prediction.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/1c3752586e7d746b13eb5b2784ac9fe53756b7fd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Petros Koumoutsakos
- Themistoklis P. Sapsis
- Pantelis R. Vlachas
- Michelle Girvan
- Jaideep Pathak
- Edward Ott
- Brian R. Hunt
- Steven L. Brunton
In Statistical and Nonlinear Physics
- Petros Koumoutsakos
- Themistoklis P. Sapsis
- Pantelis R. Vlachas
- Michelle Girvan
- Jaideep Pathak
- Edward Ott
- Brian R. Hunt
- Mark Newman
Citations in the corpus
- Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics (2019, background): “The issue has been also demonstrated in previous studies in both RC (Lu et al., 2018; Pathak, Wikner et al., 2018) and RNNs (Vlachas et al., 2018).”