Symbolic regression
This page was last updated on 2025-06-23 11:23:08 UTC
Manually curated articles on Symbolic regression
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
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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, Proceedings of the National Academy of Sciences of the United States of America | 3888 | 70 | open_in_new |
visibility_off | Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression | Patrick A. K. Reinbold, Logan Kageorge, M. Schatz, R. Grigoriev | 2021-02-24 | Nature Communications | 107 | 25 | open_in_new |
visibility_off | Data-driven discovery of coordinates and governing equations | Kathleen P. Champion, Bethany Lusch, J. Kutz, S. Brunton | 2019-03-29 | Proceedings of the National Academy of Sciences of the United States of America | 750 | 70 | open_in_new |
visibility_off | Chaos as an intermittently forced linear system | S. Brunton, Bingni W. Brunton, J. Proctor, E. Kaiser, J. Kutz | 2016-08-18 | Nature Communications | 526 | 70 | open_in_new |
visibility_off | Sparse identification of nonlinear dynamics for model predictive control in the low-data limit | E. Kaiser, J. Kutz, S. Brunton | 2017-11-15 | Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences | 523 | 70 | open_in_new |
visibility_off | Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics | N. Mangan, S. Brunton, J. Proctor, J. Kutz | 2016-05-26 | IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE Transactions on Molecular Biological and Multi-Scale Communications | 358 | 70 | open_in_new |
visibility_off | SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics | Kadierdan Kaheman, J. Kutz, S. Brunton | 2020-04-05 | Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences | 268 | 70 | open_in_new |
visibility_off | Multidimensional Approximation of Nonlinear Dynamical Systems | Patrick Gelß, Stefan Klus, J. Eisert, Christof Schutte | 2018-09-07 | Journal of Computational and Nonlinear Dynamics | 71 | 20 | open_in_new |
visibility_off | Learning Discrepancy Models From Experimental Data | Kadierdan Kaheman, E. Kaiser, B. Strom, J. Kutz, S. Brunton | 2019-09-18 | ArXiv, arXiv.org | 46 | 70 | open_in_new |
visibility_off | Discovery of Physics From Data: Universal Laws and Discrepancies | Brian M. de Silva, D. Higdon, S. Brunton, J. Kutz | 2019-06-19 | Frontiers in Artificial Intelligence | 84 | 70 | open_in_new |
visibility_off | Data-driven discovery of partial differential equations | S. Rudy, S. Brunton, J. Proctor, J. Kutz | 2016-09-21 | Science Advances | 1369 | 70 | open_in_new |
visibility_off | Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control | Urban Fasel, J. Kutz, Bingni W. Brunton, S. Brunton | 2021-11-22 | Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences | 228 | 70 | open_in_new |
visibility_off | Learning sparse nonlinear dynamics via mixed-integer optimization | D. Bertsimas, Wes Gurnee | 2022-06-01 | Nonlinear Dynamics | 46 | 95 | open_in_new |
visibility_off | A Unified Framework for Sparse Relaxed Regularized Regression: SR3 | P. Zheng, T. Askham, S. Brunton, J. Kutz, A. Aravkin | 2018-07-14 | IEEE Access | 139 | 70 | open_in_new |
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
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Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |