Early detection of Hopf bifurcations in a prototypical fluid system via deep learning of unbinarized recurrence plots.

Park J, Jeon KE, Yang Z, Xu H, Hur J, Yin B, Li LKB

DOI: 10.1063/5.0335558
Published: 2026 May 1
Container: Chaos (Woodbury, N.Y.)
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1063/5.0335558,
  title = {Early detection of Hopf bifurcations in a prototypical fluid system via deep learning of unbinarized recurrence plots.},
  author = {Park J and Jeon KE and Yang Z and Xu H and Hur J and Yin B and Li LKB},
  year = {2026},
  journal = {Chaos (Woodbury, N.Y.)},
  doi = {10.1063/5.0335558},
  url = {https://doi.org/10.1063/5.0335558}
}

RIS

TY  - JOUR
TI  - Early detection of Hopf bifurcations in a prototypical fluid system via deep learning of unbinarized recurrence plots.
AU  - Park J
AU  - Jeon KE
AU  - Yang Z
AU  - Xu H
AU  - Hur J
AU  - Yin B
AU  - Li LKB
PY  - 2026
JO  - Chaos (Woodbury, N.Y.)
DO  - 10.1063/5.0335558
UR  - https://doi.org/10.1063/5.0335558
ER  -

APA

J, P., KE, J., Z, Y., H, X., J, H., B, Y., & LKB, L. (2026). Early detection of Hopf bifurcations in a prototypical fluid system via deep learning of unbinarized recurrence plots.. Chaos (Woodbury, N.Y.). https://doi.org/10.1063/5.0335558

Source records

pubmed · retrieved 2026-09-26T01:10:51.403Z