Symbolic regression
This page was last updated on 2025-10-06 06:12:11 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 of the United States of America, Proceedings of the National Academy of Sciences | 4167 | 73 | 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 | 119 | 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 | 813 | 73 | 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 | 553 | 73 | 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 | 558 | 73 | 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 | 374 | 73 | 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 | 294 | 73 | 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 | 74 | 23 | 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 | 47 | 73 | 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 | 91 | 73 | 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 | 1449 | 73 | 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 | 265 | 73 | open_in_new |
visibility_off | Learning sparse nonlinear dynamics via mixed-integer optimization | D. Bertsimas, Wes Gurnee | 2022-06-01 | Nonlinear Dynamics | 52 | 96 | 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 | 150 | 73 | open_in_new |
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
Recommended articles on Symbolic regression
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visibility_off | Automatic Regression for Governing Equations with Control (ARGOSc) | Amir Bahador Javadi, Amin Kargarian, M. Naraghi-Pour | 2025-09-11 | ArXiv | 0 | 20 |
visibility_off | Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems | Mehrdad Anvari, H. Marasi, Hossein Kheiri | 2025-09-02 | Scientific Reports | 0 | 4 |
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visibility_off | Realizing Reduced and Sparse Biochemical Reaction Networks from Dynamics | M. Filo, M. Khammash | 2025-08-25 | ArXiv | 0 | 8 |
visibility_off | Hyperparameter Optimization in the Estimation of PDE and Delay-PDE models from data | Oliver Mai, Tim W. Kroll, Uwe Thiele, Oliver Kamps | 2025-08-18 | ArXiv | 0 | 1 |
visibility_off | DKFNet: Differentiable Kalman Filter for Field Inversion and Machine Learning | Yuan Wu, Sicheng He | 2025-09-09 | ArXiv | 0 | 0 |
visibility_off | Discovering equations from data: symbolic regression in dynamical systems | Beatriz R. Brum, L. Lober, I. Previdelli, Francisco A. Rodrigues | 2025-08-27 | ArXiv | 0 | 14 |
visibility_off | Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics | Julian Evan Chrisnanto, Salsabila Rahma Alia, Y. H. Chrisnanto, Ferry Faizal | 2025-09-16 | ArXiv | 0 | 2 |
visibility_off | Dynamic mode decomposition for detecting transient activity via sparsity and smoothness regularization | Yutaro Tanaka, Hiroya Nakao | 2025-08-14 | ArXiv | 0 | 3 |
visibility_off | Universal Learning of Nonlinear Dynamics | Evan Dogariu, Anand Brahmbhatt, Elad Hazan | 2025-08-16 | ArXiv | 0 | 62 |
visibility_off | Real-time forecasting of chaotic dynamics from sparse data and autoencoders | Elise Ozalp, Andrea N'ovoa, Luca Magri | 2025-08-12 | ArXiv | 1 | 1 |
visibility_off | Data-Driven Discovery of Emergent Dynamics in Reaction-Diffusion Systems from Sparse and Noisy Observations | Saumitra Dwivedi, Ricardo da Silva Torres, Ibrahim A. Hameed, Gunnar Tufte, Anniken Susanne T. Karlsen | 2025-09-11 | ArXiv | 0 | 1 |
visibility_off | Approximating the universal thermal climate index using sparse regression with orthogonal polynomials | Sabin Roman, Gregor Skok, L. Todorovski, S. Džeroski | 2025-08-15 | ArXiv | 0 | 65 |
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visibility_off | Noise estimation of SDE from a single data trajectory | Munawar Ali, Purba Das, Qi Feng, L. Gao, Guang Lin | 2025-09-29 | ArXiv | 0 | 1 |
visibility_off | Equation-Free Coarse Control of Distributed Parameter Systems via Local Neural Operators | Gianluca Fabiani, Constantinos I. Siettos, Ioannis G. Kevrekidis | 2025-09-28 | ArXiv | 0 | 9 |
visibility_off | Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements | Xuyang Li, Mahdi Masmoudi, Rami Gharbi, Nizar Lajnef, Vishnu Naresh Boddeti | 2025-08-29 | ArXiv | 0 | 31 |
visibility_off | Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective | Xu Shang, Yang Zheng | 2025-09-10 | ArXiv | 0 | 4 |
visibility_off | A multichannel generalization of the HAVOK method for the analysis of nonlinear dynamical systems | Carlos Colchero, Jorge Perez, Alvaro Herrera, Oliver Probst | 2025-09-16 | ArXiv | 0 | 0 |
visibility_off | Stoch-IDENT: New Method and Mathematical Analysis for Identifying SPDEs from Data | Jianbo Cui, Roy Y. He | 2025-08-26 | ArXiv | 0 | 0 |
visibility_off | Kolmogorov-Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networks | Zongyu Wu, Ruichen Xu, Luoyao Chen, Georgios Kementzidis, Siyao Wang, Yuefan Deng | 2025-08-26 | ArXiv | 0 | 2 |
visibility_off | Parameter Robustness in Data-Driven Estimation of Dynamical Systems | Ayush Pandey | 2025-09-08 | ArXiv | 0 | 0 |
visibility_off | Data-efficient Kernel Methods for Learning Hamiltonian Systems | Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi, P. Tavallali, H. Owhadi | 2025-09-21 | ArXiv | 0 | 38 |
visibility_off | Data selection: at the interface of PDE-based inverse problem and randomized linear algebra | Kathrin Hellmuth, Ruhui Jin, Qin Li, Stephen J. Wright | 2025-10-02 | ArXiv | 0 | 2 |
visibility_off | Nested Operator Inference for Adaptive Data-Driven Learning of Reduced-order Models | Nicole Aretz, Karen Willcox | 2025-08-15 | ArXiv | 0 | 2 |
visibility_off | Learning to Solve Optimization Problems Constrained with Partial Differential Equations | Yusuf Guven, Vincenzo Di Vito, Ferdinando Fioretto | 2025-09-29 | ArXiv | 0 | 12 |
visibility_off | A Review of Modern Stochastic Modeling: SDE/SPDE Numerics, Data-Driven Identification, and Generative Methods with Applications in Biology and Epidemiology | Yassine Sabbar | 2025-08-14 | ArXiv | 0 | 0 |
visibility_off | Current state and open problems in universal differential equations for systems biology | Maren Philipps, Nina Schmid, Jan Hasenauer | 2025-08-30 | NPJ Systems Biology and Applications | 0 | 3 |
visibility_off | A Score-based Diffusion Model Approach for Adaptive Learning of Stochastic Partial Differential Equation Solutions | Toan Huynh, Ruth Fajardo, Guannan Zhang, Lili Ju, Feng Bao | 2025-08-09 | ArXiv | 0 | 6 |
visibility_off | Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data | Luke McLennan, Yi Wang, R. Farell, Minh Nguyen, Chandrajit Bajaj | 2025-09-08 | ArXiv | 0 | 2 |
visibility_off | Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification | Sanket R. Jantre, Deepak Akhare, Zhiyuan Wang, Xiaoning Qian, Nathan M. Urban | 2025-08-26 | ArXiv | 0 | 6 |
visibility_off | Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems | Aleksandra Jekic, Afroditi Natsaridou, S. Riemer-Sørensen, Helge Langseth, Odd Erik Gundersen | 2025-09-24 | ArXiv | 0 | 22 |
visibility_off | Learning Spatio-Temporal Dynamics via Operator-Valued RKHS and Kernel Koopman Methods | Mahishanka Withanachchi | 2025-08-23 | ArXiv | 0 | 0 |
visibility_off | Probabilistic and nonlinear compressive sensing | Lukas Silvester Barth, Paulo von Petersenn | 2025-09-18 | ArXiv | 0 | 1 |
visibility_off | Solving Inverse Gardner–Kawahara Problems with Physics-Informed Neural Networks: A Data-Driven Approach | Mazaher Kabiri, Sanam Sabooni | 2025-09-16 | Physica Scripta | 0 | 0 |
visibility_off | Time-Varying Autoregressive Models: A Novel Approach Using Physics-Informed Neural Networks | Zhixuan Jia, Chengcheng Zhang | 2025-09-01 | Entropy | 0 | 1 |
visibility_off | Forecasting Continuous Non-Conservative Dynamical Systems in SO(3) | Lennart Bastian, Mohammad Rashed, N. Navab, Tolga Birdal | 2025-08-11 | ArXiv | 1 | 55 |
visibility_off | Uncertainty Propagation Networks for Neural Ordinary Differential Equations | Hadi Jahanshahi, Zheng H. Zhu | 2025-08-22 | ArXiv | 0 | 2 |
visibility_off | Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks | Yongsheng Chen, Wei Guo, Qi Tang, Xinghui Zhong | 2025-08-16 | ArXiv | 0 | 2 |
visibility_off | Investigating the use of physics informed neural networks for dam-break scenarios | Kinza Mumtaz, Muhammad Waasif Nadeem, Adnan Khan, Zahra Lakdawala | 2025-09-19 | PLOS One | 0 | 2 |
Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |