Early detection of Hopf bifurcations in a prototypical fluid system via deep learning of unbinarized recurrence plots
- DOI
- 10.1063/5.0335558
- Published
- 2026-05-01
- Container
- Chaos: An Interdisciplinary Journal of Nonlinear Science
- Publisher
- AIP Publishing
- 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 = {Jungjin Park and Kang Eun Jeon and Zhijian Yang and Haoming Xu and Jun Hur and Bo Yin and Larry K. B. Li},
year = {2026},
journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
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 - Jungjin Park AU - Kang Eun Jeon AU - Zhijian Yang AU - Haoming Xu AU - Jun Hur AU - Bo Yin AU - Larry K. B. Li PY - 2026 JO - Chaos: An Interdisciplinary Journal of Nonlinear Science DO - 10.1063/5.0335558 UR - https://doi.org/10.1063/5.0335558 ER -
APA
Park, J., Jeon, K. E., Yang, Z., Xu, H., Hur, J., Yin, B., & Li, L. K. B. (2026). Early detection of Hopf bifurcations in a prototypical fluid system via deep learning of unbinarized recurrence plots. Chaos: An Interdisciplinary Journal of Nonlinear Science. https://doi.org/10.1063/5.0335558
Source records
- crossref · retrieved 2026-11-10T06:33:44.571Z