Symbolic regression

This page was last updated on 2026-11-09 06:26:36 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. L. 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 5777 83 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, Roman O. Grigoriev 2021-02-24 Nature Communications 165 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 1132 83 open_in_new
visibility_off Chaos as an intermittently forced linear system S. Brunton, Bingni W. Brunton, J. L. Proctor, E. Kaiser, J. Kutz 2016-08-18 Nature Communications 691 83 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 719 83 open_in_new
visibility_off Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics N. Mangan, Steve Brunton, J. L. Proctor, J. Kutz 2016-05-26 IEEE Transactions on Molecular Biological and Multi-Scale Communications, IEEE Transactions on Molecular, Biological and Multi-Scale Communications 445 83 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 420 83 open_in_new
visibility_off Multidimensional Approximation of Nonlinear Dynamical Systems Patrick Gelß, Stefan Klus, J. Eisert, C. Schütte 2018-09-07 Journal of Computational and Nonlinear Dynamics 87 47 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 83 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 108 83 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 431 83 open_in_new
visibility_off Learning sparse nonlinear dynamics via mixed-integer optimization D. Bertsimas, Wes Gurnee 2022-06-01 Nonlinear Dynamics 83 102 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 192 85 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 Data-Driven Discovery of High-Dimensional Dynamical Systems with Sparse Interpretable Neural Networks S. Xing, Qing-Yu Han, E. Charalampidis, Ying-Cheng Lai 2026-09-29 PRX Intelligence 1 16
visibility_off Neural SINDy: robust discovery of governing equations using neural ODEs and sparse identification Min Jae Jeon, Davin Lee, J. Byun 2026-11-01 Nonlinear Dynamics 0 9
visibility_off Variational integration for robust data-driven inference of noisy and unstable dynamical systems Zhi-Qi Zhang, Pei Wang 2026-10-01 Nonlinear Dynamics 0 5
visibility_off SIPHy: Sparse identification of port-Hamiltonian systems from noisy data Håkon Noren Myhr, Sølve Eidnes, J. Kutz 2026-09-17 ArXiv 1 83
visibility_off ChemODE: A Physics-Informed Neural Surrogate for Robust Pharmacokinetics and Biochemical Dynamics Wit Kulvutiroj 2026-09-30 ACS Omega 0 0
visibility_off Stochastic Physics-Constrained Operator Inference for Complex Systems Chang-Hong Mou 2026-09-27 ArXiv 0 1
visibility_off Machine-learned spectral PDE solvers for fluids and plasmas Mellow Baixuan Chen, A. Kaptanoglu 2026-10-19 ArXiv 0 14
visibility_off Tensor sparse identification of differential equations of nonlinear dynamical systems Denis M. Tikhonov, V. Strijov 2026-09-11 Modeling and Analysis of Information Systems 0 9
visibility_off Autoencoders vs. Numerical Analysis-Informed Manifold Learning for Navier-Stokes Flows A. Della Pia, Lucia Russo, I. Kevrekidis, Constantinos I. Siettos 2026-09-21 ArXiv 0 7
visibility_off Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems R. Seabourne, Alice Cicirello 2026-10-11 ArXiv 0 3
visibility_off To lift or not to lift: Operator inference for nonlinear PDEs with boundary control Zhuoyuan Wang, Yorie Nakahira, Wael H. Ali, Saviz Mowlavi 2026-11-04 ArXiv 0 13
visibility_off Higher-Order Kolmogorov-Arnold Networks for Dynamics Kevin Slote, Jeremie Fish 2026-10-02 ArXiv 0 6
visibility_off Identifying ODEs from Unstructured Data with Causal Representation Learning Alessandro Trenta, Riccardo Massidda, D. Bacciu, Sara Magliacane 2026-09-29 ArXiv 0 29
visibility_off py-ARGOS: A Python package for automatic, statistically principled discovery of ordinary differential equations from data Yu-Zhen Zhang, Rui Carvalho 2026-10-25 ArXiv 0 4
visibility_off Linearized PINN with pretrained nonlinear layers Wen-Hao Chen, Alexandre M. Tartakovsky 2026-09-14 ArXiv 0 4
visibility_off A two-step physics-informed neural network approach for open-loop optimal control of the Gardner equation with limited data and partially unknown parameters Sana Idrees, Shahbaz Ahmad 2026-11-05 Machine Learning for Computational Science and Engineering 0 1
visibility_off Neural-Operator-Predicted Time-Dependent Reduced Subspaces for Projection-Based Simulation of Nonlinear PDEs Bo-Xi Song, Rennie Mirollo, J. Engelbrecht 2026-10-06 ArXiv 0 20
visibility_off Self-averaging enables reconstruction of higher-order chaotic networks Jia-Hao He, Xiao-Qun Wu, Zi-Ye Fan, Jian-Wen Feng, Ting-Wen Huang 2026-10-22 ArXiv 0 34
visibility_off Learning stochasticity via a nonparametric approach to state-dependent noise estimation G. Pillonetto, A. Giaretta, M. Bisiacco 2026-09-16 Nature Communications 0 40
visibility_off Petrov-Galerkin operator inference with application to stability-encouraging identification J. Rettberg, Jonas Nicodemus, Harsh Sharma, Boris Kramer, Jörg Fehr, Benjamin Unger 2026-10-01 ArXiv 1 6
visibility_off Finite-Data Error Bounds for Approximating the Koopman Operator: Sampling Measures, Super-Polynomial Convergence and Regularization D. Fassler, Rachel Morris, Jason J. Bramburger, Simone Brugiapaglia 2026-09-25 ArXiv 0 14
visibility_off NEXT: Physics-Informed Neuro-Spectral Exponential Time Differencing Architectures Márcio Marques, Leonardo Mendonça, Leonardo M. Moreira, Christian Júnior de Oliveira, Vitor Balestro, Tiago Novello, Daniel Yukimura, Pavel S. Petrov, Lucas Nissenbaum 2026-09-25 ArXiv 0 10
visibility_off Integral chemical reaction neural networks Abraham Reyes-Velazquez, S. Güttel 2026-09-18 ArXiv 0 26
visibility_off Dynamics-informed machine learning for recovering extensive missing systems dynamics Tao Wu, Xiang-Yun Gao, Ying Tang, Feng An, Xiao-Tian Sun, Kazuyuki Aihara, Hai-Zhong An, Jürgen Kurths 2026-09-19 Nature Communications 0 15
visibility_off Practical Algebraic Parameter Estimation for Noisy Data via Gaussian Process Regression Oren Bassik, Alexander Demin, Alexey Ovchinnikov 2026-09-24 ArXiv 0 19
visibility_off Learning Cole-Hopf Coordinates for Stochastic Growth Dynamics: the KPZ class Lorenzo Mensi, Simone Cuonzo, M. C. Angelini, S. Chibbaro, Cyril Furtlehner, A. Rosso 2026-10-22 ArXiv 0 37
visibility_off Resolving Structural Uncertainty in Stochastic Dynamical Systems via Ensemble-Based Bayesian Inference Francesco Zambelli, Valeria d'Andrea, Manlio De Domenico 2026-10-14 ArXiv 0 16
visibility_off Automated discovery of dimensional reductions in coupled oscillators Dishant Sisodia, S. Jalan 2026-09-30 ArXiv 0 2
visibility_off ZENO: Nonlinear filtering with Zakai Equation Neural Operators Aaditya Prasad, S. Flavell, Brian D. DePasquale 2026-10-23 ArXiv 0 25
visibility_off Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders J. N. N'konzi, F. Nüske, Stefan Klus 2026-10-08 ArXiv 0 18
visibility_off Learning Closure of Dynamical Systems with Kernel Ridge Regression Evan Habbershaw, Jonathan Harlim, Senwei Liang 2026-10-01 ArXiv 0 13
visibility_off Discovering like a scientist: Literature-informed dimensionless learning for physical closure of data-driven equations Hao Xu, Tian-Hao Wu, Dong-Xiao Zhang 2026-11-04 ArXiv 0 15
visibility_off An Adjoint Krylov Approach to Parameter Inference for Affine Families of Linear Evolution Equations Aditya Dendukuri, Shivkumar Chandrasekaran, Linda R. Petzold 2026-10-11 ArXiv 0 7
visibility_off Physics-Informed Nonlinear Vector Autoregressive Models for the Prediction of Dynamical Systems J. Adler, Samuel Hocking, Xiao-Zhe Hu, Shafiqul Islam 2026-09-30 SIAM Journal on Scientific Computing 0 25
visibility_off Spatio-temporal Latent Denoising Diffusion Probabilistic Models for Reduced-order Modeling of Parametrized Dynamical Systems Michiel Nikken, Nicolò Botteghi, Federico Califano, S. Glas 2026-09-15 ArXiv 0 8
visibility_off Reinforcement learning for control of partial differential equations Nicolò Botteghi, Matteo Tomasetto, Andrea Manzoni 2026-10-29 ArXiv 0 5
visibility_off Inference of Unknown Dynamical Components Using Next Generation Reservoir Computing: From Chaotic Systems to Climate Data Jule Budnick, Andrew Keane, Serhiy Yanchuk 2026-09-21 ArXiv 0 39
visibility_off Hybrid AI-Mathematical Modelling for Nonlinear Dynamical Systems H. Kumar, Prabhat Kumar, Vinod Kumar, J. Masih, Happy Singh, R. Kumar, Shyam Lal 2026-10-23 Mathematics for Applications 0 2
visibility_off A Practical Tutorial on Physics‐Informed Networks for Pharmacometrics and Quantitative Systems Pharmacology Nazanin Ahmadi Daryakenari, M. Kohandel 2026-09-22 CPT: Pharmacometrics & Systems Pharmacology 0 29
visibility_off Fractional-order nonlinear system identification: a comprehensive review Mohammad Jahani Moghaddam 2026-10-21 International Journal of Dynamics and Control 0 3
visibility_off Efficient Closure Learning in Dynamical Systems: A Comparison of Data-Assimilated and Embedded Approaches R. Bandy, Teresa Portone, Mohammad Khalil, Eric Crislip, Kyle Neal 2026-11-02 ArXiv 0 2
visibility_off Data‐Driven Discovery of Classical and Anomalous Transport Models From Sparse Breakthrough Curves in Heterogeneous Porous Media via Symbolic Regression Jin-Woo Im, F. D. de Barros, S. Masri, Muhammad Sahimi 2026-11-04 Journal of Geophysical Research: Machine Learning and Computation 0 62
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index