Symbolic regression
This page was last updated on 2026-05-18 07:39:39 UTC
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 | 4911 | 80 | 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 | 138 | 26 | 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 | 974 | 80 | 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 | 624 | 80 | 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 | 658 | 80 | 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 | 417 | 80 | 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 | 345 | 80 | 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 | 81 | 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.org, ArXiv | 55 | 80 | 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 | 103 | 80 | 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 | 1643 | 80 | 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 | 347 | 80 | open_in_new |
| visibility_off | Learning sparse nonlinear dynamics via mixed-integer optimization | D. Bertsimas, Wes Gurnee | 2022-06-01 | Nonlinear Dynamics | 65 | 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 | 177 | 80 | open_in_new |
| Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
Recommended articles on Symbolic regression
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |
|---|---|---|---|---|---|---|
| visibility_off | Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms | Zhenhua Dang, Lei Zhang, Long Wang, G. He | 2026-04-20 | ArXiv | 0 | 6 |
| visibility_off | WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos | Cristian López, Mckenna Partridge, S. D. Pascuale, J. Lore, Andrew J. Christlieb, Stephen Becker, D. Bortz | 2026-04-25 | ArXiv | 0 | 9 |
| visibility_off | A Robust SINDy Autoencoder for Noisy Dynamical System Identification | Kai Ding | 2026-04-06 | ArXiv | 0 | 1 |
| visibility_off | Efficacy of the Weak Formulation of Sparse Nonlinear Identification in Predicting Vortex-Induced Vibrations | Haimi Jha, H. Saddal, C. Bose | 2026-03-29 | ArXiv | 0 | 3 |
| visibility_off | PowerSINDy: Identifying Nonlinear Time-Dependent Dynamics in Power Grid Frequency | Xinyi Wen, Xiao Li, L. R. Gorjão, V. Hagenmeyer, Benjamin Schafer | 2026-05-04 | ArXiv | 0 | 12 |
| visibility_off | One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators | Teng-Yang Ma, Luca Rosafalco, Wei Cui, Lin Zhao, A. Frangi | 2026-04-16 | ArXiv | 0 | 13 |
| visibility_off | From Data to Laws: Neural Discovery of Conservation Laws Without False Positives | Rahul Ray | 2026-03-20 | ArXiv | 0 | 2 |
| visibility_off | AC-SINDy: Compositional Sparse Identification of Nonlinear Dynamics | Peter Racioppo | 2026-04-20 | ArXiv | 0 | 1 |
| visibility_off | A neural operator framework for data-driven discovery of stability and receptivity in physical systems | Chengyun Wang, Liwei Chen, Nils Thuerey | 2026-04-21 | ArXiv | 0 | 3 |
| visibility_off | Data-driven discovery of polynomial ODEs with provably bounded solutions | A. Alcalde, Giovanni Fantuzzi | 2026-04-29 | ArXiv | 0 | 2 |
| visibility_off | A machine learning framework for uncovering stochastic nonlinear dynamics from noisy data | Matteo Bosso, Giovanni Franzese, K. Swamy, M. Theulings, Alejandro M. Aragón, F. Alijani | 2026-04-07 | ArXiv | 0 | 30 |
| visibility_off | Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data | Xingyu Chen, Junxiu An, Junjian Guo, Yuqian Zhou | 2026-03-23 | ArXiv | 0 | 3 |
| visibility_off | Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning | Salah A. Faroughi, Farinaz Mostajeran, Amirhossein Arzani, S. Faroughi | 2026-03-25 | ArXiv | 1 | 26 |
| visibility_off | EqOD: Symmetry-Informed Stability Selection for PDE Identification | Gnankan Landry Regis N'guessan, Bum Jun Kim | 2026-05-12 | ArXiv | 0 | 2 |
| visibility_off | Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations | Qi Shao, Duxin Chen, Jiawen Chen, Yujie Zeng, Athen Ma, Wenwu Yu, V. Latora, Wei Lin | 2026-04-01 | ArXiv | 0 | 18 |
| visibility_off | Discovery of Nonlinear Dynamics with Automated Basis Function Generation | Mohammad Amin Basiri, Charles Nicholson | 2026-05-10 | ArXiv | 0 | 3 |
| visibility_off | Data-driven discovery and control of multistable nonlinear systems and hysteresis via structured Neural ODEs | I. G. Salas, Ethan King | 2026-03-27 | ArXiv | 0 | 1 |
| visibility_off | Uncertainty-Aware Sparse Identification of Dynamical Systems via Bayesian Model Averaging | Shuhei Kashiwamura, Yusuke Kato, Hiroshi Kori, Masato Okada | 2026-04-12 | ArXiv | 0 | 2 |
| visibility_off | SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems | Murat Furkan Mansur, T. Kumbasar | 2026-04-16 | ArXiv | 1 | 24 |
| visibility_off | Weak-PDE-Net: Discovering Open-Form PDEs via Differentiable Symbolic Networks and Weak Formulation | Xinxin Li, Xin Cui, Jingyang Qi, Juan Zhang, Da Li, Junping Yin | 2026-03-24 | ArXiv | 0 | 1 |
| visibility_off | Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data | Vedanta Thapar, Abhinav Gupta | 2026-04-19 | ArXiv | 0 | 2 |
| visibility_off | Sparse Weak-Form Discovery of Stochastic Generators | A. EshwarR., G. Honnavar | 2026-03-21 | ArXiv | 0 | 7 |
| visibility_off | Learning interpretable and stable dynamical models via mixed-integer Lyapunov-constrained optimization | Zhe Li, Ilias Mitrai | 2026-04-08 | ArXiv | 0 | 8 |
| visibility_off | Data-Driven Tensor Decomposition Identification of Homogeneous Polynomial Dynamical Systems | Xin Mao, Joshua Pickard, Can Chen | 2026-04-03 | ArXiv | 0 | 4 |
| visibility_off | Noise Impact on System Identification Using Genetic Programming-Based Symbolic Regression | Lele Zhang | 2026-03-23 | 2026 International Russian Smart Industry Conference (SmartIndustryCon) | 0 | 1 |
| visibility_off | The finite expression method for turbulent dynamics with high-order moment recovery | Xingjian Xu, D. Qi, Chunmei Wang | 2026-05-11 | ArXiv | 0 | 15 |
| visibility_off | Optimizing Reservoir Computing for Reconstructing Ergodic Properties | A. Kawano, Ilia Soroka, Greg J. Stephens | 2026-05-02 | ArXiv | 0 | 3 |
| visibility_off | Bayesian hypergraph inference from scarce and noisy dynamical observations | Karen E. S. Tang, Vivek Srikrishnan, Jackson Kulik | 2026-05-05 | ArXiv | 0 | 15 |
| visibility_off | Bias Inheritance in Neural-Symbolic Discovery of Constitutive Closures Under Function-Class Mismatch | Han-Yu Liang, Ze Tao, Fujun Liu | 2026-04-01 | ArXiv | 0 | 5 |
| visibility_off | Revealing dynamics of non-autonomous complex systems from data | Chengzuo Zhuge, Zhen Jiang, Zhefan Xu, Wei Chen | 2026-05-10 | ArXiv | 0 | 15 |
| visibility_off | Equation-Free Digital Twins for Nonlinear Structural Dynamics | M. Abaei, A. Bahootoroody, Arttu Polojarvi, H. Remes, U. T. Tygesen, Mikko Suominen, Michael Beer | 2026-05-01 | ArXiv | 0 | 32 |
| visibility_off | Fast and principled equation discovery from chaos to climate | Yuzhen Zhang, Weizhen Li, R. Carvalho | 2026-04-13 | ArXiv | 0 | 5 |
| visibility_off | Discovery of Symbolic Hamiltonian Expressions with Buckingham-Symplectic Networks | Joe Germany, Joseph Bakarji, Sara Najem | 2026-04-01 | ArXiv | 0 | 9 |
| visibility_off | WGFINNs: Weak formulation-based GENERIC formalism informed neural networks | Jun Sur Richard Park, A. Hashim, S. W. Cheung, Youngsoo Choi, Yeonjong Shin | 2026-04-03 | ArXiv | 0 | 3 |
| visibility_off | Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations | Zhao Wei, Kenneth Hor Cheng Koh, S. Chin, J. Chan, Chin Chun Ooi, Y. Ong | 2026-05-05 | ArXiv | 0 | 10 |
| visibility_off | Watch your neighbors: Training statistically accurate chaotic systems with local phase space information | Joon-Hyuk Ko, A. Giraldo, Deok-Sun Lee | 2026-05-14 | ArXiv | 0 | 10 |
| visibility_off | Interpretable Physics Extraction from Data for Linear Dynamical Systems using Lie Generator Networks | Shafayeth Jamil, R. Kapadia | 2026-03-28 | ArXiv | 1 | 31 |
| visibility_off | PINNs for Stochastic Dynamics: Modeling Brownian Motion via Verlet Integration | Y. Herry, Julian Evan, Jeremia Oktavian, Ferry Faizal | 2026-04-08 | International Journal of Information Technology and Computer Science | 0 | 2 |
| visibility_off | Geometric structure of ideal data-driven dynamical model using RfR method | Natsuki Tsutsumi, Kengo Nakai, Yoshitaka Saiki | 2026-04-12 | ArXiv | 0 | 5 |
| visibility_off | Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation | Sumie Song, Bong Gyun Shin, Jae Yong Lee | 2026-05-08 | ArXiv | 0 | 8 |
| visibility_off | Data-driven identification of chaotic nonlinear systems using local maximum entropy surrogates | N. Raza, Faegheh Moazeni | 2026-03-31 | Nonlinear Dynamics | 0 | 12 |
| visibility_off | GasNiTROM: Model Reduction via Non-Intrusive Optimization of Oblique Projection Operators and Guaranteed-Stable Latent-Space Dynamics | Cole J. Errico, Alberto Padovan, Daniel J. Bodony | 2026-03-22 | ArXiv | 0 | 4 |
| visibility_off | Accelerating the Simulation of Ordinary Differential Equations Through Physics-Preserving Neural Networks | Andrew C. Tagg, Andrew Frandsen, An Ning | 2026-05-07 | ArXiv | 0 | 5 |
| visibility_off | Comparing Physics-Informed and Neural ODE Approaches for Modeling Nonlinear Biological Systems: A Case Study Based on the Morris-Lecar Model | N. Matzakos, Chrisovalantis Sfyrakis | 2026-03-27 | ArXiv | 1 | 4 |
| visibility_off | Anticipating tipping in spatiotemporal systems with machine learning | S. Deb, Zheng-Meng Zhai, Mulugeta A. Haile, Ying-Cheng Lai | 2026-04-07 | ArXiv | 0 | 9 |
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |