Symbolic regression

This page was last updated on 2026-07-20 07:17:47 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, Proceedings of the National Academy of Sciences of the United States of America 5205 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 148 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 1023 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 645 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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A 681 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 429 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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A 369 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 82 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 59 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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A 370 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 183 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 An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications Y. Li, A. Larrañaga, Steven L. Brunton, Urban Fasel 2026-07-16 ArXiv 0 10
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 Data-Driven Discovery of Governing Physical Laws Using Scientific Machine Learning Grace Yao 2026-06-24 2026 23rd International Joint Conference on Computer Science and Software Engineering (JCSSE) 0 6
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 1 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 Robust Sparse Identification of Nonlinear Dynamics via Least Trimmed Squares F. Amaral, G. N. Grapiglia, C. Oishi 2026-06-26 ArXiv 0 17
visibility_off Data-Driven Identification of Stochastic Dynamical Systems Rishav Jha, Kameshwar Sahani, S. K. Sahani, R. Raj, D. Sah 2026-05-23 African Multidisciplinary Journal of Sciences and Artificial Intelligence 0 9
visibility_off Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization Hao Xu, Siyu Lou, Yuntian Chen, Dongxiao Zhang 2026-06-29 ArXiv 0 19
visibility_off Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure Samuel Ahnert, E. Lagemann, H. J. Bae, Kunihiko Taira, Ricardo Vinuesa, Christian Lagemann, Steven L. Brunton 2026-06-27 ArXiv 0 5
visibility_off Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression Max Kreider, John Harlim, Daning Huang 2026-06-30 ArXiv 1 3
visibility_off Model discovery for dynamical systems with complex-valued product units Martin Brückmann, B. Dellen, Uwe Jaekel 2026-05-26 ArXiv 0 2
visibility_off Identifying nonlinear dynamical systems using subset regression. Weizhen Li, Qiang Fu, Yifan Hong, Haojian Lu 2026-07-15 Scientific reports 0 16
visibility_off From data chaos to physically interpretable deterministic mapping. Dongni Jia, Shuai Li, Xinyi Zuo, Haibo Shi, Junyi Wang, Xiaofeng Zhou 2026-07-13 Nature communications 0 14
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 CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification Bishal Chhetri, B. Kumar 2025-10-20 Bulletin of Mathematical Biology 0 1
visibility_off Neural Discovery of Memory and Nonlocal Kernels in Integro-Differential Equations with Constrained Kolmogorov--Arnold Networks Aruzhan Tleubek, Salah A. Faroughi 2026-07-13 ArXiv 0 2
visibility_off Weak form Scientific Machine Learning for Systems Biology: A Tutorial on WENDy N. Heitzman-Breen, Rainey Lyons, Paras Jain, M. Jolly, David M. Bortz 2026-07-03 bioRxiv 0 61
visibility_off Semi-analytical hierarchical Bayesian inference of nonlinear model structure in stochastic dynamics: Applied to compartmental models of infectious diseases B. Robinson, Philippe Bisaillon, R. Sandhu, Mohammad Khalil, J. Edwards, T. Kendzerska, Thomas Walker, Shirley Mills, C. Pettit, D. Poirel, A. Sarkar 2026-07-10 PLOS One 0 30
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 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 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 1 4
visibility_off Frequency-Domain Neural ODEs for Modeling Non-Linear Dynamical Systems Mohammed Ashraf, Ayman Elbadawy 2026-06-20 ArXiv 0 2
visibility_off Discovering Latent Response Laws in Forced Physical Systems Yi Zhu, Su Chen, Xiaojun Li, Xiuli Du 2026-07-09 ArXiv 0 6
visibility_off OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems Till Richter, Niki Kilbertus 2026-06-17 ArXiv 0 17
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 PRONE: Petrov-Galerkin Operator Learning Unifies DMD, SINDy&Koopmanism Matthew J. Colbrook, April Herwig, J. Kutz 2026-06-26 ArXiv 0 7
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 Generalized Forcing Method: Generation of Diverse Data for Training Linear Transport PDE Closure Models Wenyuan Xue, Ali Mani 2026-06-03 ArXiv 0 7
visibility_off PDE Identification Using Noise Adaptive Differentiation in Strong Form (S-IDENT) Roy Y. He, S. Kang 2026-06-30 ArXiv 0 3
visibility_off Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Aviral Prakash, M. Klasky 2026-07-13 ArXiv 0 4
visibility_off Learning network topology from partial observations in nonlinear dynamical systems Mingming Xiao, Chengpu Yu 2026-07-01 Nonlinear Dynamics 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 Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections Nicholas Zolman, Sajeda Mokbel, Samuel E. Otto, S. Brunton 2026-06-04 ArXiv 0 80
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 A Data-Assimilation-Augmented Optimization Framework for Parameter Estimation in Dynamical Systems Muhammad Jalil Ahmad, A. Biswas, Kathleen Hoffman 2026-06-28 ArXiv 0 20
visibility_off Scalable Bayesian Inference for Nonlinear Conservation Laws Tim Weiland, Philipp Hennig 2026-05-29 ArXiv 0 5
visibility_off Augmented-state-space-based nonparametric dynamical modeling of non-autonomous nonlinear systems Pengpeng Liu, Yang Guo, Yegao Qu 2026-07-01 Nonlinear Dynamics 0 6
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 A Koopman-PINN Framework for Epidemic Models: Parameter Inference and Forecasting Achraf Zinihi, Matthias Ehrhardt, M. Ammi 2026-06-13 ArXiv 0 14
visibility_off Physics-Informed Compressed Sensing: Incorporating Rotor Dynamic Equations for High-Fidelity State Estimation from Sub-Nyquist Samples Arian Sardari 2026-05-24 ICC 2026 - IEEE International Conference on Communications 0 0
visibility_off Identifying sensitivity-dominant parameters via active subspaces in reduced-order modeling of fluid dynamics Dewu Yang, Rui Wang, Pengyu Lai, Junjie Wang, Feng Wang, Huiyun Xu 2026-06-01 Nonlinear Dynamics 0 16
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index