Symbolic regression

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Manually curated articles on Symbolic regression

Abstract	Title	Authors	Publication Date	Journal/ Conference	Citation count	Highest h-index	View recommendations
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 of the United States of America, Proceedings of the National Academy of Sciences	4773	79	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	135	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	949	79	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	612	79	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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A	643	79	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	408	79	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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A	334	79	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	80	27	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	54	79	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	102	79	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	1615	79	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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A	331	79	open_in_new
visibility_off	Learning sparse nonlinear dynamics via mixed-integer optimization	D. Bertsimas, Wes Gurnee	2022-06-01	Nonlinear Dynamics	62	98	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	171	79	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