Symbolic regression

This page was last updated on 2026-06-22 08:18:38 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 5063 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 144 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 1005 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 632 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 671 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 426 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 364 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, arXiv.org 56 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 104 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 361 80 open_in_new
visibility_off Learning sparse nonlinear dynamics via mixed-integer optimization D. Bertsimas, Wes Gurnee 2022-06-01 Nonlinear Dynamics 69 99 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 181 80 open_in_new
Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index View recommendations

Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index
visibility_off How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit A. Larrañaga, Urban Fasel, Steven L. Brunton 2026-06-10 ArXiv 0 10
visibility_off WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos Cristian López, M. Partridge, S. D. Pascuale, J. Lore, Andrew J. Christlieb, Stephen Becker, D. Bortz 2026-04-25 ArXiv 0 9
visibility_off Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements Filippo Zacchei, A. Larrañaga, A. Frangi, A. Manzoni, S. Brunton 2026-06-14 ArXiv 0 80
visibility_off Data-driven discovery of governing differential equations across physical systems Siyu Lou, Hao Xu, Wenguang Wang, Lu Lu, Hao Sun, Yang Liu, Linfeng Zhang, Dongxiao Zhang, Yuntian Chen 2026-06-08 ArXiv 0 12
visibility_off Data-Driven Identification of Stochastic Dynamical Systems Rishav Jha, Kameshwar Sahani, S. K. Sahani, R. Raj, Dilip Kumar Sah 2026-05-23 African Multidisciplinary Journal of Sciences and Artificial Intelligence 0 9
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 Discovering interpretable low-dimensional dynamics using maximum entropy Michael C. Chung, T. Mohan, P. Dixit, Juan Guan 2026-05-16 ArXiv 0 20
visibility_off Discovery of Nonlinear Dynamics with Automated Basis Function Generation Mohammad Amin Basiri, Charles Nicholson 2026-05-10 ArXiv 0 3
visibility_off EqOD: Symmetry-Informed Stability Selection for PDE Identification Gnankan Landry Regis N'guessan, Bum Jun Kim 2026-05-12 ArXiv 0 8
visibility_off Model discovery for dynamical systems with complex-valued product units M. Bruckmann, B. Dellen, Uwe Jaekel 2026-05-26 ArXiv 0 2
visibility_off An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts Umut Demir, Aamir Ahmad, Walter Fichter 2026-06-11 ArXiv 0 2
visibility_off Quasi-potential of stochastic dynamics via instanton-guided sparse identification Leonardo de Souza Grigorio, Mnerh Alqahtani 2026-05-26 Journal of Physics A: Mathematical and Theoretical 0 2
visibility_off Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs Pongpisit Thanasutives, Naichang Ke, Y. Kawahara 2026-05-26 ArXiv 0 24
visibility_off Pyphysdisc: co-evolutionary symbolic regression with adaptive smoothing windows for autonomous physical law discovery from noisy data Ali Tozar 2026-05-01 International Journal of Dynamics and Control 0 2
visibility_off From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models Conor Rowan 2026-06-08 ArXiv 0 3
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 Sparse Approximation Method for Accurate Uncertainty Propagation through a Nonlinear System Amit Jain, Puneet Singla, Roshan Eapen 2026-06-01 The Journal of the Astronautical Sciences 0 4
visibility_off A likelihood-based framework for simultaneously learning both noise and growth dynamics using biologically-informed neural networks Rebecca M. Crossley, R. Baker 2026-06-11 ArXiv 0 5
visibility_off Optimizing Reservoir Computing for Reconstructing Ergodic Properties A. Kawano, Ilia Soroka, Greg J. Stephens 2026-05-02 ArXiv 0 3
visibility_off A Dynamic Subspace Approach for Low-rank Approximation of Large-scale Nonlinear Systems J. Dechant, R. Geelen, Shane A. McQuarrie, Johann Guilleminot 2026-05-25 ArXiv 0 6
visibility_off Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning P. Muratore, Mackenzie W. Mathis 2026-06-11 ArXiv 0 30
visibility_off A Quadratic Order Reduction -- Gaussian Process Ordinary Differential Equation framework for the inference of Large Continuous Dynamical Systems Guglielmo Padula, M. Girfoglio, G. Rozza 2026-06-11 ArXiv 0 55
visibility_off Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error Markus Gross 2026-05-29 ArXiv 0 2
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 Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials Vojtvech Votruba, Zequn He, Weilun Qiu, Celia Reina, Michal Pavelka 2026-05-09 ArXiv 0 4
visibility_off A Data-Free Symbolic Regression Approach for Solving Equations S. Garmaev, Vinay Sharma, Olga Fink 2026-06-05 ArXiv 0 3
visibility_off Learning partially observed systems with neural Hamiltonian ordinary differential equations Sunniva Meltzer, Sølve Eidnes, Alexander J. Stasik 2026-05-22 ArXiv 0 9
visibility_off Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks J. Szereszewski, F. Fainstein, Leandro E. Fernandez, G. Mindlin 2026-06-07 ArXiv 0 32
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 Learning dynamical systems with biochemically informed neural ordinary differential equations Luis L. Fonseca, Reinhard C. Laubenbacher, Lucas Böttcher 2026-05-22 bioRxiv 0 7
visibility_off Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Moyi Tian, D. Messenger, Vanja Dukic, Nancy Rodríguez, David M. Bortz 2026-05-28 ArXiv 0 4
visibility_off A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery Zhang Jiang, Marios Andreou, Sebastian Reich, Nan Chen 2026-04-28 ArXiv 0 3
visibility_off PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability Federico J. Gonzalez 2026-05-07 ArXiv 0 4
visibility_off Calibrated Intrusive Reduced-Order Model of Burgers’ Equation Using a Combination of Proper Orthogonal Decomposition and LSTM Deep Learning Algorithm Mina Golzar, M. K. Moayyedi, Faranak Fotouhi-Ghazvini, M. Vahabi, Hossein Fotouhi 2026-05-09 Modelling 0 12
visibility_off PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes S. Dabral 2026-05-26 ArXiv 0 0
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 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 Reduced-order modeling of nonlinear multiscale industrial systems via sparse regression in latent representations. Dongni Jia, Xiaofeng Zhou, Shuai Li, Haibo Shi, Linzhi Li 2026-05-20 Scientific reports 0 14
visibility_off Nonlinear extensions and coordinate selection for operator-based causality analysis, with application to chaotic wake flows V. Jiménez, S. L. Clainche, Ankit Srivastava, S. Dawson 2026-05-01 Journal of Physics: Conference Series 0 20
visibility_off Control-oriented cluster-based reduced-order modelling P. Olivucci, David E Rival, Richard Semaan 2026-04-28 ArXiv 0 4
visibility_off Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data K. Martini, Eslam Abdelaleem, Paarth Gulati, Ilya Nemenman 2026-04-27 ArXiv 1 4
visibility_off Physics-Informed Neural Networks and Radial Basis Functions for PDEs with Dirac Delta Sources Manuel Reyna, A. Tartakovsky 2026-06-10 ArXiv 0 1
visibility_off Reservoir computing for system identification and model predictive control. Jan P. Williams, J. N. Kutz, Krithika Manohar 2026-05-01 Neural networks : the official journal of the International Neural Network Society 0 14
visibility_off Learning regime-dependent governing equations: A symbolic decision tree approach Ilias Mitrai, Tong Liu, Gabriel E. Sanoja 2026-05-22 ArXiv 0 16
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index