Symbolic regression

This page was last updated on 2026-09-14 06:39:17 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 5434 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 157 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 1060 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 671 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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A 701 76 open_in_new
visibility_off Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics , Steve 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 441 57 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 398 76 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 85 34 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 59 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 105 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. Mathematical, Physical, and Engineering Sciences, Proceedings of the Royal Society A 397 76 open_in_new
visibility_off Learning sparse nonlinear dynamics via mixed-integer optimization D. Bertsimas, Wes Gurnee 2022-06-01 Nonlinear Dynamics 74 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 188 78 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 Dynamics-aware identification of governing equations from sparse and noisy data Pongpisit Thanasutives, Yoshinobu Kawahara 2026-07-31 ArXiv 1 11
visibility_off Symbolic Neural ODEs: Learning interpretable models from time-series data N. Boddupalli, J. Moehlis 2026-08-22 ArXiv 0 7
visibility_off Hybrid SINDy-EnKF in Learning Chikungunya Dynamics from Incomplete, Noisy or Partially Observed Data B. A. Afful, Changhong Mou, Luis F. Gordillo 2026-07-29 ArXiv 0 7
visibility_off Data-driven linear analysis of dynamical systems via nonlinearity-subtracted dynamic mode decomposition Benjamín Herrmann, Katherine Cao, S. Brunton, B. McKeon 2026-08-13 ArXiv 0 76
visibility_off Data-Driven Modeling of Nonlinear Dynamical Systems with Machine Learning Ming-Yang Wang 2026-09-01 Theoretical and Natural Science 0 0
visibility_off Dynamic mode decomposition with multiple initial conditions for linearizing nonlinear partial differential equations Kanav Singh Rana, Nitu Kumari 2026-08-03 Engineering with Computers 0 19
visibility_off Attractor Geometry Determines the Identifiability Limits of System Discovery Matteo Gallo, Fabio Anselmi, Paolo Lazzari 2026-07-20 ArXiv 2 4
visibility_off Data-Driven Learning of Unknown Nonlinear Differential Equations Using Functional Analysis Seyyed Shaho Alaviani, , G. Vogl 2026-09-03 ArXiv 0 6
visibility_off A Sobolev–Information Perspective on Derivative-Observation-Augmented PINNs for Parameter Identification of Second-Order Dynamical Systems Liwen Xu, Yi-Xuan Lin 2026-08-09 Axioms 0 3
visibility_off Dynamic Reduced-Order Data Assimilation from Sparse Velocity Measurements Mauricio Portilla, F. Galarce, Ernesto Castillo, Benjamín Herrmann 2026-09-09 ArXiv 0 7
visibility_off Stochastic Operator Inference for reduced-order modeling of capillary wave turbulence using experimental measurements Hyeonghun Kim, Lei Zhang, James R. Friend, Boris Kramer 2026-09-03 ArXiv 0 9
visibility_off Differential-Embedding Reconstruction of Dynamical Systems from Scalar Time Series A. Shaa, C. Guet 2026-08-17 ArXiv 0 10
visibility_off A zero-one law for one-shot system identification N. Boullé, Diana Halikias, Samuel E. Otto, Alex Townsend 2026-07-17 ArXiv 1 32
visibility_off Linear and Nonlinear Latent-Space Reduced-Order Models for the Rayleigh--Taylor Instability Téo Granger, B. Nadiga, B. Gréa, A. Briard, Paul Creusy 2026-08-27 ArXiv 0 14
visibility_off Origins and mitigation of double descent in reduced order modeling A. Klishin, J. Kutz, Krithika Manohar 2026-07-29 ArXiv 1 57
visibility_off On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement Baptiste Mathevon, Farah Cherfaoui, Amaury Habrard, M. Sebban 2026-07-26 ArXiv 0 28
visibility_off An interpretable data-driven identification of dynamical systems via universal neural ordinary differential equations Qing-Tong Dong 2026-08-03 Engineering Research Express 0 0
visibility_off Freeze, Then Select: Structured Field Adapters and Stability-Validated Weak Selection for PDE Discovery from Sparse Observations Juncheng Zhong, Chen Shen, Jian-Feng Liu, Z. Xiao, Longjiu Luo, Qianrong Wang, Wenjun Xu, Wenlian Lu 2026-07-31 ArXiv 0 5
visibility_off Data-driven reduced modeling of neural dynamics A. Marraffa, R. Krause, V. Mante, G. Haller 2026-07-28 Nature Communications 0 16
visibility_off Structured Neural Chaos: An Adaptive Surrogate Modeling Framework for Functional Uncertainty Quantification and Global Sensitivity Analysis Isabel Corona Guevara, Ye-Ping Hu 2026-07-31 ArXiv 0 2
visibility_off Koopman-based model predictive control for nonlinear systems with bounded model uncertainty Yangzhe Liu, Hanqiu Bao 2026-08-25 International Journal of Dynamics and Control 0 7
visibility_off Dynamics Creation through Neural Dynamical Transfer Learning Hekun Ma, Qi-Yang Ge, Yu Meng, C. Grebogi, Wei Lin 2026-09-09 ArXiv 0 25
visibility_off A Mathematical Model for Predicting Complex Dynamic Systems Using Hybrid Computational Approaches George Em Karniadakis, E. Torfs, L. Marchetti 2026-07-28 Global Synthesis in Education Journal 0 18
visibility_off Physics-Guided Spectral Parametric Reduced-Order Modeling for Transient Prediction of Controlled Dynamical Systems Ao Zhang, Tian Zhang, A. Cammi, Xiang Wang 2026-07-20 ArXiv 0 38
visibility_off Learning and Predicting the Nonlinear Variability of X-ray Binaries with the Koopman Operator Eric Miao, R. Shang, K. Mori, Reshmi Mukherjee Columbia Astrophysics Laboratory, Columbia University, D. Physics, Astronomy, Barnard College 2026-09-01 ArXiv 0 87
visibility_off Gaussian process learning with flow map refinement for parameter estimation in dynamical systems Yue Hao, Dongwei Ye 2026-08-23 ArXiv 0 13
visibility_off On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems I. Horenko 2026-07-30 ArXiv 0 27
visibility_off Hybrid Analytical Numerical and Machine Learning Frameworks for Solving Deterministic and Stochastic Differential Equations with Stability, Convergence, and Uncertainty Quantification Suresh Kumar Sahani 2026-07-23 Journal of Intelligent Decision Making and Information Science 2 3
visibility_off KOOPMAN-Luenberger Observer Design for Nonlinear Systems with Application to the Monitoring of a Latent Thermal Energy Storage Mustapha Habib, Dario Aguiar, Esther Kieseritzky, T. Barz, Qian Wang 2026-08-11 ArXiv 0 22
visibility_off Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions James Crowley, Faez Ahmed, A. van Beek 2026-08-31 ArXiv 0 7
visibility_off Going with the flow to solve for symmetry-driven PDE dynamics with physics-informed neural networks M. Kavousanakis, Gianluca Fabiani, A. Georgiou, Constantinos I. Siettos, P. Kevrekidis, I. Kevrekidis 2026-08-05 Nature Communications 0 56
visibility_off Learning piecewise-smooth dynamical systems D. Murari, Erik Jansson, Chris Budd Obe, C. Schönlieb 2026-08-20 ArXiv 0 28
visibility_off Overcoming error-in-variable or stiffness problem in SINDy-like data-driven model discovery L. Fung 2026-07-18 ACM Transactions on AI for Science 0 7
visibility_off Modelisation of chaotic systems with a latent Stochastic Differential Equation Ismaël Zighed, Nicolas Thome, Patrick Gallinari, T. Sayadi 2026-08-04 ArXiv 0 14
visibility_off Weak-form Extended Dynamic Mode Decomposition Christopher W. Curtis, David M. Bortz 2026-07-28 ArXiv 0 4
visibility_off Positive-Allocation Companion Predictors for Nonlinear Dynamics and Their Finite-Difference Diagnostics Cynthia Flores, James E. Pascoe 2026-07-17 ArXiv 0 4
visibility_off Real-time inverse solutions via neural matrix operators Julie V. Pham, Thomas O’Leary-Roseberry, Omar Ghattas, K. Willcox 2026-08-25 ArXiv 0 5
visibility_off Data-driven reconstruction of dynamical systems using Takens'Theorem, manifold learning, and universal function approximators Maximilian Topel, Andrew L. Ferguson 2026-08-05 ArXiv 0 13
visibility_off A Hybrid Physics-Augmented Neural Network for Dynamic System Modeling with Partially Known Dynamics Laurin Ludmann, Jaeyoun Choi, J. Neubeck, Andreas Wagner, Chuchu Fan 2026-07-27 Vehicles 0 6
visibility_off Learning neural evolution operators: from decoding to identifiable causal state-space models Armin Hakkak Moghadam Torbati 2026-07-31 Journal of Neural Engineering 0 3
visibility_off Inertial Manifold Neural Operator for Dissipative Time-Dependent Partial Differential Equations Xiaoyan Xie, Clarence W. Rowley 2026-08-24 ArXiv 0 2
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index