Symbolic regression

This page was last updated on 2025-11-10 06:13:19 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 4297 75 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 122 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 835 75 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 570 75 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 576 75 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 389 75 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 302 75 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 75 24 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 75 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 92 75 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 275 75 open_in_new
visibility_off Learning sparse nonlinear dynamics via mixed-integer optimization D. Bertsimas, Wes Gurnee 2022-06-01 Nonlinear Dynamics 54 97 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 154 75 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 Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems Jiaqi Yao, Lewis Mitchell, John Maclean, Hemanth Saratchandran 2025-09-17 ArXiv 0 7
visibility_off MDBench: Benchmarking Data-Driven Methods for Model Discovery Amirmohammad Ziaei Bideh, Aleksandra Georgievska, Jonathan Gryak 2025-09-24 ArXiv 0 1
visibility_off Interpretable neural network system identification method for two families of second-order systems based on characteristic curves Federico J. Gonzalez, Luis P. Lara 2025-09-12 Nonlinear Dynamics 0 1
visibility_off Hierarchical Physics-Embedded Learning for Spatiotemporal Dynamical Systems Xizhe Wang, Xiaobin Song, Qingshan Jia, Hongbo Zhao, Benben Jiang 2025-10-29 ArXiv 0 3
visibility_off Sparse identification of epidemiological compartment models with conserved quantities M. Aminian, Kristin M. Kurianski 2025-10-22 ArXiv 0 5
visibility_off Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systems Elias Al Ghazal, J. Mounayer, Beatriz Moya, Sebastian Rodriguez, C. Ghnatios, F. Chinesta 2025-10-21 ArXiv 0 15
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 3
visibility_off MODE: Learning compositional representations of complex systems with Mixtures Of Dynamical Experts Nathan Quiblier, Roy Friedman, Matthew Ricci 2025-10-10 ArXiv 0 2
visibility_off A Novel Reservoir Computing Framework for Chaotic Time Series Prediction Using Time Delay Embedding and Random Fourier Features S. K. Laha 2025-11-04 ArXiv 0 0
visibility_off CBINNS: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification Bishal Chhetri, B. V. R. Kumar 2025-10-20 ArXiv 0 1
visibility_off White-box machine learning for uncovering physically interpretable dimensionless governing equations for granular materials Xu Han, Lu Jing, C. Y. Kwok, Gengchao Yang, Y. D. Sobral 2025-09-18 ArXiv 0 2
visibility_off Latent Twins Matthias Chung, Deepanshu Verma, Max Collins, Amit N. Subrahmanya, V. Sastry, Vishwas Rao 2025-09-24 ArXiv 0 5
visibility_off PDE-Free Mass-Constrained Learning of Complex Systems with Hidden States: The crowd dynamics case Gianmaria Viola, Alessandro Della Pia, Lucia Russo, Ioannis G. Kevrekidis, Constantinos I. Siettos 2025-10-20 ArXiv 0 5
visibility_off Automated Constitutive Model Discovery by Pairing Sparse Regression Algorithms with Model Selection Criteria Jorge-Humberto Urrea-Quintero, David Anton, L. Lorenzis, Henning Wessels 2025-09-19 ArXiv 2 56
visibility_off A Novel Scientific Machine Learning Method for Epidemiological Modelling Tirtha Tilak Pani, R. Dandekar, Prathamesh Dinesh Joshi, R. Dandekar, S. Panat 2025-09-15 2025 IEEE International Conference on eScience (eScience) 0 5
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 Self-induced stochastic resonance: A physics-informed machine learning approach Divyesh Savaliya, Marius E. Yamakou 2025-10-26 ArXiv 0 10
visibility_off Towards Interpretable Deep Learning and Analysis of Dynamical Systems via the Discrete Empirical Interpolation Method Hojin Kim, R. Maulik 2025-10-22 ArXiv 0 27
visibility_off HYCO: Hybrid-Cooperative Learning for Data-Driven PDE Modeling Lorenzo Liverani, Matthys J. Steynberg, Enrique Zuazua 2025-09-17 ArXiv 1 3
visibility_off Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations Tyrus Whitman, Andrew Particka, Christopher Diers, Ian Griffin, Charuka D. Wickramasinghe, Pradeep K. Ranaweera 2025-10-28 ArXiv 0 2
visibility_off Next-Generation Reservoir Computing for Dynamical Inference Rok Cestnik, E. A. Martens 2025-09-14 ArXiv 0 7
visibility_off An Introductory Guide to Koopman Learning Matthew J. Colbrook, Zlatko Drmavc, Andrew Horning 2025-10-24 ArXiv 0 19
visibility_off Integrating Score-Based Generative Modeling and Neural ODEs for Accurate Representation of Multiscale Chaotic Dynamics Giulio Del Felice, L. T. Giorgini 2025-11-05 ArXiv 0 7
visibility_off Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI Yoh-ichi Mototake, Makoto Sasaki 2025-11-06 ArXiv 0 8
visibility_off Reconstructing High-fidelity Plasma Turbulence with Data-driven Tuning of Diffusion in Low Resolution Grids Kunpeng Li, Youngwoo Cho, X. Garbet, Chenguang Wan, R. Varennes, K. Lim, V. Grandgirard, Zhisong Qu, Ong Yew Soon 2025-09-15 ArXiv 0 5
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 Data-Driven Reduced Modeling of Recurrent Neural Networks Alice Marraffa, Renate Krause, Valerio Mante, George Haller 2025-10-14 bioRxiv 0 0
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 Leveraging Scale Separation and Stochastic Closure for Data-Driven Prediction of Chaotic Dynamics Ismaël Zighed, Nicolas Thome, Patrick Gallinari, T. Sayadi 2025-10-28 ArXiv 0 13
visibility_off Floating-Body Hydrodynamic Neural Networks Tianshuo Zhang, Wenzhe Zhai, Rui Yann, Jia Gao, He Cao, Xianglei Xing 2025-09-17 ArXiv 0 3
visibility_off Reparameterizing 4DVAR with neural fields Jaemin Oh 2025-09-26 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 Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning Haoran Li, Chenhan Xiao, Muhao Guo, Yang Weng 2025-10-04 ArXiv 0 10
visibility_off InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics Ann Huang, Mitchell Ostrow, Satpreet H. Singh, L. Kozachkov, I. Fiete, Kanaka Rajan 2025-10-29 ArXiv 0 34
visibility_off From Observations to Parameters: Detecting Changepoint in Nonlinear Dynamics with Simulation-based Inference Xiangbo Deng, Cheng Chen, Peng Yang 2025-10-20 ArXiv 0 1
visibility_off A novel reservoir computing for inferring hyperchaotic systems from partial observation Yuting Li, Yong Li 2025-09-30 Nonlinear Dynamics 0 1
visibility_off Weak Form Learning for Mean-Field Partial Differential Equations: an Application to Insect Movement Seth Minor, B. Elderd, Benjamin Van Allen, David M. Bortz, Vanja Dukic 2025-10-09 ArXiv 0 22
visibility_off Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-Sørensen, Helge Langseth, Odd Erik Gundersen 2025-09-24 ArXiv 0 17
visibility_off Physics-based deep kernel learning for parameter estimation in high dimensional PDEs Weihao Yan, Christoph Brune, Mengwu Guo 2025-09-17 ArXiv 0 1
visibility_off A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations Stefano Giampiccolo, Giovanni Iacca, Luca Marchetti 2025-10-03 bioRxiv 0 5
visibility_off Learning to Predict Chaos: Curriculum-Driven Training for Robust Forecasting of Chaotic Dynamics Harshil Vejendla 2025-10-05 ArXiv 0 0
visibility_off Control of dynamical systems with neural networks Lucas Bottcher 2025-10-06 ArXiv 0 0
visibility_off Statistical Parameter Calibration with the Generalized Fluctuation Dissipation Theorem and Generative Modeling L. T. Giorgini, Tobias Bischoff, Andre N. Souza 2025-09-24 ArXiv 3 7
visibility_off Position: Biology is the Challenge Physics-Informed ML Needs to Evolve Julien Martinelli 2025-10-29 ArXiv 0 0
visibility_off Equilibrium flow: From Snapshots to Dynamics Yanbo Zhang, Michael Levin 2025-09-22 ArXiv 0 4
visibility_off Machine Learning of Nonlinear Waves: Data-Driven Methods for Computer-Assisted Discovery of Equations, Symmetries, Conservation Laws, and Integrability J. Adriazola, Panayotis Kevrekidis, V. Koukouloyannis, Wei Zhu 2025-10-16 ArXiv 0 12
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index