Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems.

Chepuri R, Amzalag D, Antonsen TM, Girvan M

DOI: 10.1063/5.0206232
Published: 2024 Jun 1
Container: Chaos (Woodbury, N.Y.)
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1063/5.0206232,
  title = {Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems.},
  author = {Chepuri R and Amzalag D and Antonsen TM and Girvan M},
  year = {2024},
  journal = {Chaos (Woodbury, N.Y.)},
  doi = {10.1063/5.0206232},
  url = {https://doi.org/10.1063/5.0206232}
}

RIS

TY  - JOUR
TI  - Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems.
AU  - Chepuri R
AU  - Amzalag D
AU  - Antonsen TM
AU  - Girvan M
PY  - 2024
JO  - Chaos (Woodbury, N.Y.)
DO  - 10.1063/5.0206232
UR  - https://doi.org/10.1063/5.0206232
ER  -

APA

R, C., D, A., TM, A., & M, G. (2024). Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems.. Chaos (Woodbury, N.Y.). https://doi.org/10.1063/5.0206232

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pubmed · retrieved 2026-09-26T11:13:39.772Z