Grading your models: Assessing dynamics learning of models using persistent homology-Reference-Cited by-同舟云学术

Grading your models: Assessing dynamics learning of models using persistent homology

Published:2021-12 Issue:12 Volume:31 Page:123109
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Tan Eugene¹^ORCID,Corrêa Débora¹²^ORCID,Stemler Thomas¹^ORCID,Small Michael¹²^ORCID

Affiliation:

1. Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Crawley, Western Australia 6009, Australia

2. ARC Centre for Transforming Maintenance Through Data Science, The University of Western Australia, Crawley, Western Australia 6009, Australia

Funder

Centre for Transforming Maintenance through Data Science

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0073722

Reference38 articles.

1. H. Jaeger, “The “echo state” approach to analysing and training recurrent neural networks—With an erratum note,” Gesellschaft fur Mathematik und Datenverarbeitung mbH (German National Research Center for Information Technology, GMD) Technical Report, Bonn, Germany, 2001, Vol. 148, p. 13.

2. T. Jüngling, T. Lymburn, T. Stemler, D. Corrêa, D. Walker, and M. Small, “Reconstruction of complex dynamical systems from time series using reservoir computing,” in Proceedings of 2019 IEEE International Symposium on Circuits and Systems (IEEE, 2019), pp. 1–5.

3. Minimum description length neural networks for time series prediction

4. Robustness of LSTM neural networks for multi-step forecasting of chaotic time series

5. Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network

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