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Recommendations for the article Chaos as an intermittently forced linear system
| Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index |
|---|---|---|---|---|---|---|
| visibility_off | Featurizing Koopman mode decomposition for robust forecasting | D. Aristoff, J. Copperman, Nathan Mankovich, Alexander E. Davies | 2023-12-14 | The Journal of Chemical Physics | 2 | 12 |
| visibility_off | Deep learning delay coordinate dynamics for chaotic attractors from partial observable data | Charles D. Young, M. Graham | 2022-11-20 | Physical review. E | 16 | 52 |
| visibility_off | Machine learning predictions from unpredictable chaos | Jian Jiang, Long Chen, Lu Ke, Bozheng Dou, Yueying Zhu, Yazhou Shi, Huahai Qiu, Bengong Zhang, Tianshou Zhou, Guo-Wei Wei | 2025-03-19 | ArXiv | 0 | 6 |
| visibility_off | DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation. | Hao Peng, Pei Chen, R. Liu | 2020-05-16 | Chaos | 8 | 91 |
| visibility_off | DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation. | Hao Peng, Pei Chen, R. Liu | 2020-05-16 | Chaos | 8 | 91 |
| visibility_off | Detecting chaos in lineage-trees: A deep learning approach | H. Rappeport, Irit Levin Reisman, Naftali Tishby, N. Balaban | 2021-06-08 | ArXiv | 4 | 56 |
| visibility_off | Cluster-based network modeling—From snapshots to complex dynamical systems | Daniel Fernex, B. R. Noack, R. Semaan | 2021-06-01 | Science Advances | 63 | 52 |
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |