Symbolic regression

This page was last updated on 2025-11-24 06:12:58 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 4332 76 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 123 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 841 76 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 572 76 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 578 76 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 76 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 305 76 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 76 25 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 48 76 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 76 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 278 76 open_in_new
visibility_off Learning sparse nonlinear dynamics via mixed-integer optimization D. Bertsimas, Wes Gurnee 2022-06-01 Nonlinear Dynamics 55 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 155 76 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 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 Differentiable Sparse Identification of Lagrangian Dynamics Zi-Rui Zhang, Hao Sun 2025-11-13 ArXiv 0 1
visibility_off Sparse identification of epidemiological compartment models with conserved quantities M. Aminian, Kristin M. Kurianski 2025-10-22 ArXiv 0 6
visibility_off A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series Xuyang Li, J. Harlim, R. Maulik 2025-11-10 ArXiv 0 27
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 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 Physics-Informed Machine Learning for Characterizing System Stability Tomoki Koike, Elizabeth Qian 2025-11-11 ArXiv 0 2
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 Rediscovering shallow water equations from experimental data Kjell S. Heinrich, Douglas S. Seth, Mats Ehrnstrom, S. Ellingsen 2025-11-07 ArXiv 0 18
visibility_off CODE: A global approach to ODE dynamics learning Nils Wildt, D. Tartakovsky, S. Oladyshkin, Wolfgang Nowak 2025-11-19 ArXiv 0 49
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 Self-induced stochastic resonance: A physics-informed machine learning approach Divyesh Savaliya, Marius E. Yamakou 2025-10-26 ArXiv 0 10
visibility_off Globalizing the Carleman linear embedding method for nonlinear dynamics I. Novikau, Ilon Joseph 2025-10-17 ArXiv 0 3
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 Data-Driven System Identification of High-Speed Craft Dynamics Uncovering Governing Equations with Machine Learning Subodh Chander, Stefano Brizzolara 2025-10-17 SNAME International Conference on Fast Sea Technology 0 0
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 An Introductory Guide to Koopman Learning Matthew J. Colbrook, Z. Drmač, Andrew Horning 2025-10-24 ArXiv 0 21
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 8
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 Noise estimation of SDE from a single data trajectory Munawar Ali, Purba Das, Qi Feng, L. Gao, Guang Lin 2025-09-29 ArXiv 0 1
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 Non-intrusive structural-preserving sequential data assimilation Lizuo Liu, Tongtong Li, Anne Gelb 2025-10-22 ArXiv 0 2
visibility_off Learning stochasticity: a nonparametric framework for intrinsic noise estimation G. Pillonetto, A. Giaretta, M. Bisiacco 2025-11-17 ArXiv 0 39
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 Integral Bayesian symbolic regression for optimal discovery of governing equations from scarce and noisy data Oriol Cabanas-Tirapu, Sergio Cobo-López, Savannah E. Sanchez, Forest L. Rohwer, M. Sales-Pardo, R. Guimerà 2025-11-18 ArXiv 0 36
visibility_off Spectrum and Physics-Informed Neural Networks (SaPINNs) for Input-State-Parameter Estimation in Dynamic Systems Subjected to Natural Hazards-Induced Excitation Antonina M. Kosikova, Apostolos F. Psaros, Andrew Smyth 2025-11-10 ArXiv 0 13
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 Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data Manuel Palma Banos, J. Kress, R. Hernandez, G. Craven 2025-10-23 ArXiv 0 42
visibility_off Reparameterizing 4DVAR with neural fields Jaemin Oh 2025-09-26 ArXiv 0 0
visibility_off When is a System Discoverable from Data? Discovery Requires Chaos Zakhar Shumaylov, Peter Zaika, Philipp Scholl, Gitta Kutyniok, L. Horesh, C. Schonlieb 2025-11-12 ArXiv 0 55
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 1 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 Data selection: at the interface of PDE-based inverse problem and randomized linear algebra Kathrin Hellmuth, Ruhui Jin, Qin Li, Stephen J. Wright 2025-10-02 ArXiv 0 2
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 On-line learning of dynamic systems: sparse regression meets Kalman filtering G. Pillonetto, Akram Yazdani, A. Aravkin 2025-11-14 ArXiv 0 59
visibility_off Universal differential equations as a unifying modeling language for neuroscience A. El‐Gazzar, M. Gerven 2025-10-30 Frontiers in Computational Neuroscience 0 39
visibility_off Lax-Pair-FIND: Discovering Lax pair from scarce data via deep learning. Shuning Lin, Yong Chen 2025-11-01 Chaos 0 6
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 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 Biomolecular Motion: The Physics-Informed Machine Learning Paradigm Aaryesh Deshpande 2025-11-10 ArXiv 0 0
visibility_off Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models Manuel Hinz, Maximilian Mauel, Patrick Seifner, David Berghaus, K. Cvejoski, Ramsés J. Sánchez 2025-10-14 ArXiv 1 8
visibility_off Learning to Predict Chaos: Curriculum-Driven Training for Robust Forecasting of Chaotic Dynamics Harshil Vejendla 2025-10-05 ArXiv 0 1
visibility_off Control of dynamical systems with neural networks Lucas Böttcher 2025-10-06 ArXiv 0 2
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