Symbolic regression

This page was last updated on 2026-03-02 06:28:56 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 4632 78 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 129 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 918 78 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 601 78 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 624 78 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 404 78 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 321 78 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 79 26 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 53 78 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 99 78 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 310 78 open_in_new
visibility_off Learning sparse nonlinear dynamics via mixed-integer optimization D. Bertsimas, Wes Gurnee 2022-06-01 Nonlinear Dynamics 60 98 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 162 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 Sparse Identification of Nonlinear Distributed-Delay Dynamics via the Linear Chain Trick Mohammed Alanazi, Majid Bani-Yaghoub 2026-01-20 ArXiv 0 2
visibility_off KANDy: Kolmogorov-Arnold Networks and Dynamical System Discovery Kevin Slote, Jeremie Fish, Erik Bollt 2026-02-23 ArXiv 0 4
visibility_off Knowledge-Informed Kernel State Reconstruction for Interpretable Dynamical System Discovery Luca Muscarnera, Silas Ruhrberg Est'evez, Samuel Holt, Evgeny S. Saveliev, M. Schaar 2026-01-29 ArXiv 0 73
visibility_off Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization Grant Norman, Conor Rowan, K. Maute, Alireza Doostan 2026-02-13 ArXiv 0 55
visibility_off An adjoint method for training data-driven reduced-order models Donglin Liu, Francisco Garc'ia Atienza, Mengwu Guo 2026-01-12 ArXiv 0 1
visibility_off KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra Li Su, Jun Shu, Rui Liu, Deyu Meng, Zongben Xu 2026-02-15 ArXiv 0 3
visibility_off Inverse problems for history-enriched linear model reduction Arjun Vijaywargiya, G. Biros 2026-01-11 ArXiv 0 51
visibility_off Data-driven stochastic reduced-order modeling of parametrized dynamical systems Andrew F. Ilersich, Kevin Course, Prasanth B. Nair 2026-01-15 ArXiv 0 3
visibility_off Time-Delayed Transformers for Data-Driven Modeling of Low-Dimensional Dynamics A. Alcalde, Markus Widhalm, Emre Yılmaz 2026-02-09 ArXiv 0 5
visibility_off Symbolic recovery of PDEs from measurement data Erion Morina, P. Scholl, Martin Holler 2026-02-17 ArXiv 0 4
visibility_off Physics-guided curriculum learning for the identification of reaction-diffusion dynamics from partial observations Hanyu Zhou, Yuansheng Cao, Yaomin Zhao 2026-01-24 ArXiv 0 2
visibility_off Uncertainty Quantification in Data-Driven Dynamical Models via Inverse Problem Solving Mohamed Akrout, Dan Wilson 2026-02-23 ArXiv 0 16
visibility_off SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks J. M. Minoza 2026-01-15 ArXiv 0 4
visibility_off An Empirical Investigation of Neural ODEs and Symbolic Regression for Dynamical Systems Panayiotis Ioannou, P. Liò, P. Cicuta 2026-01-28 ArXiv 0 46
visibility_off Scaling Law of Neural Koopman Operators Abulikemu Abuduweili, Yuyang Pang, Feihan Li, Changliu Liu 2026-02-23 ArXiv 0 11
visibility_off Modeling Batch Crystallization under Uncertainty Using Physics-informed Machine Learning Dingqi Nai, Huayu Li, M. Grover, Andrew J Medford 2026-02-06 ArXiv 0 30
visibility_off Learning Stiff Dynamical Operators: Scaling, Fast-Slow Excitation, and Eigen-Consistent Neural Models M. Valorani 2026-01-04 ArXiv 0 26
visibility_off Physics as the Inductive Bias for Causal Discovery Jianhong Chen, Naichen Shi, Xubo Yue 2026-02-03 ArXiv 0 1
visibility_off Streaming Operator Inference for Model Reduction of Large-Scale Dynamical Systems Tomoki Koike, Prakash Mohan, M. H. D. Frahan, Julie Bessac, E. Qian 2026-01-17 ArXiv 0 9
visibility_off Turning mechanistic models into forecasters by using machine learning Amit K. Chakraborty, Hao Wang, Pouria Ramazi 2026-02-04 ArXiv 0 15
visibility_off Out-of-Distribution Generalization for Neural Physics Solvers Zhao Wei, Chin Chun Ooi, Jian Cheng Wong, Abhishek Gupta, P. Chiu, Y. Ong 2026-01-27 ArXiv 0 33
visibility_off Deep Robust Koopman Learning from Noisy Data Aditya Singh, Rajpal Singh, J. Keshavan 2026-01-05 ArXiv 0 9
visibility_off Topological Entropy Correlates with the Predictive Power of Multiplexed Ensemble Reservoir Computing Suvankar Halder, Christopher M. Kim, V. Periwal 2026-02-07 bioRxiv 0 31
visibility_off Neuro-Symbolic Multitasking: A Unified Framework for Discovering Generalizable Solutions to PDE Families Yipeng Huang, Dejun Xu, Zexin Lin, Zhenzhong Wang, Min Jiang 2026-02-12 ArXiv 0 10
visibility_off Differentiable Modeling for Low-Inertia Grids: Benchmarking PINNs, NODEs, and DP for Identification and Control of SMIB System Shinhoo Kang, Sangwook Kim, Se-Young Yun 2026-02-10 ArXiv 0 4
visibility_off Toward Adaptive Non-Intrusive Reduced-Order Models: Design and Challenges Amirpasha Hedayat, Alberto Padovan, Karthik Duraisamy 2026-02-11 ArXiv 0 6
visibility_off Minimal realization time-delay Koopman analysis for stochastic dynamical system identification Biqi Chen, Ying Wang 2026-02-04 Advances in Structural Engineering 0 5
visibility_off Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data Leonardo Bettini, Amirhossein Kazemipour, Robert K. Katzschmann, George Haller 2026-01-06 ArXiv 0 25
visibility_off Phase-IDENT: Identification of Two-phase PDEs with Uncertainty Quantification Edward Yang, Roy Y. He 2026-01-17 ArXiv 0 3
visibility_off Latent-Variable Learning of SPDEs via Wiener Chaos Sebastian Zeng, A. Petersson, Wolf-gang Bock 2026-02-12 ArXiv 0 3
visibility_off Reduced-order complex network modeling of parametric fluid dynamic systems under partial observability Zihao Wang, Guiyong Zhang, T. Sun, Zhe Sun, Zhifan Zhang, Bo Zhou 2026-01-26 Nonlinear Dynamics 0 25
visibility_off High-Fidelity Modeling of Stochastic Chemical Dynamics on Complex Manifolds: A Multi-Scale SIREN-PINN Framework for the Curvature-Perturbed Ginzburg-Landau Equation Julian Evan Chrisnanto, Salsabila Rahma Alia, Nurfauzi Fadillah, Y. H. Chrisnanto 2026-01-13 ArXiv 0 3
visibility_off Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations Heechang Kim, Q. Cao, Hyomin Shin, Seungchul Lee, G. Karniadakis, Minseok Choi 2026-02-13 ArXiv 0 13
visibility_off From Basins to safe sets: a machine learning perspective on chaotic dynamics David Valle, Alexandre Wagemakers, M. Sanju'an 2026-01-29 ArXiv 0 3
visibility_off HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning Y. Shaaban, S. Lahlou, A. Sayed 2026-02-26 ArXiv 0 9
visibility_off Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows Xiao Xue, Tianyue Yang, Mingyang Gao, Leyu Pan, Maida Wang, Kewei Zhu, Shuo Wang, Jiuling Li, M. T. Eikelder, Peter V. Coveney 2026-02-17 ArXiv 0 6
visibility_off LEVDA: Latent Ensemble Variational Data Assimilation via Differentiable Dynamics Phillip Si, Peng Chen 2026-02-23 ArXiv 0 2
visibility_off Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling Xinghao Dong, Huchen Yang, Jin-long Wu 2026-02-19 ArXiv 0 2
visibility_off Controlled oscillation modeling using port-Hamiltonian neural networks M. Linares, Guillaume Doras, Thomas H'elie 2026-02-17 ArXiv 0 7
visibility_off Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data N. Hagelaars, G. V. Otterdijk, S. Moradi, Roland Tóth, N. Jaensson, M. Schoukens 2026-01-23 ArXiv 0 17
visibility_off Foundation Inference Models for Ordinary Differential Equations Maximilian Mauel, Johannes R. Hubers, David Berghaus, Patrick Seifner, Ramsés J. Sánchez 2026-02-09 ArXiv 0 5
visibility_off Optimal Control-Based Falsification of Learnt Dynamics via Neural ODEs and Symbolic Regression Lasse Kotz, Jonas Sjoberg, Knut AAkesson 2026-01-18 ArXiv 0 3
visibility_off PHDME: Physics-Informed Diffusion Models without Explicit Governing Equations Kaiyuan Tan, Kendra Givens, Peilun Li, Thomas Beckers 2026-01-29 ArXiv 0 4
visibility_off Compressing Complexity: A Critical Synthesis of Structural, Analytical, and Data-Driven Dimensionality Reduction in Dynamical Networks Zebiao Li, Xueying Wu, Chengyi Tu 2026-01-19 ArXiv 1 3
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index