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Recommendations for the article Data-driven discovery of partial differential equations
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index |
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visibility_off | Machine discovery of partial differential equations from spatiotemporal data: A sparse Bayesian learning framework. | Ye Yuan, Xiuting Li, Liang Li, Frank J. Jiang, Xiuchuan Tang, Fumin Zhang, Jorge Gonçalves, Henning U. Voss, Han Ding, Jürgen Kurths | 2023-11-01 | Chaos | 4 | 11 |
visibility_off | Machine Discovery of Partial Differential Equations from Spatiotemporal Data | Ye Yuan, Junlin Li, Liang Li, Frank Jiang, Xiuchuan Tang, Fumin Zhang, Sheng Liu, J. Gonçalves, H. Voss, Xiuting Li, J. Kurths, Han Ding | 2019-09-15 | ArXiv | 9 | 108 |
visibility_off | Discovering governing equations from data by sparse identification of nonlinear dynamical systems | S. Brunton, J. Proctor, J. Kutz | 2015-09-11 | Proceedings of the National Academy of Sciences | 3502 | 68 |
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visibility_off | Supplementary material from "Learning partial differential equations via data discovery and sparse optimization" | Hayden Schaeffer | 2017-01-16 | 0 | 16 | |
visibility_off | Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology | J. Kutz, Samuel H. Rudy, A. Alla, S. Brunton | 2017-12-01 | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) | 12 | 68 |
Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |