Symbolic regression
This page was last updated on 2026-09-07 06:38:31 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 | , , | 2015-09-11 | Proceedings of the National Academy of Sciences of the United States of America, Proceedings of the National Academy of Sciences | 5399 | 0 | 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 | 156 | 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 | 1056 | 80 | 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 | 663 | 80 | 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 | 700 | 80 | 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 | 439 | 80 | 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 | 397 | 80 | open_in_new |
| visibility_off | Multidimensional Approximation of Nonlinear Dynamical Systems | Patrick Gelß, Stefan Klus, J. Eisert, C. Schutte | 2018-09-07 | Journal of Computational and Nonlinear Dynamics | 84 | 33 | 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 | 80 | 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 | 80 | open_in_new |
| visibility_off | Data-driven discovery of partial differential equations | S. Rudy, S. Brunton, J. Proctor, J. Kutz | 2016-09-21 | Science Advances | 1754 | 80 | 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 | 395 | 80 | 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, | 2018-07-14 | IEEE Access | 187 | 80 | open_in_new |
| Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
Recommended articles on Symbolic regression
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |
|---|---|---|---|---|---|---|
| visibility_off | An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications | Y. Li, A. Larrañaga, Steven L. Brunton, Urban Fasel | 2026-07-16 | ArXiv | 0 | 10 |
| 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 | From data chaos to physically interpretable deterministic mapping | Dongni Jia, Shuai Li, Xinyi Zuo, Haibo Shi, Junyi Wang, Xiaofeng Zhou | 2026-07-13 | Nature Communications | 0 | 16 |
| 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 | 80 |
| 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 | NestyNet. IV. Laws Chosen by Nothing in Advance | Rodrigo Ibata, Wassim Tenachi, F. Diakogiannis, N. Ibata, A. Shankar | 2026-08-21 | ArXiv | 0 | 13 |
| visibility_off | Identifying nonlinear dynamical systems using subset regression. | Weizhen Li, Qiang Fu, Yifan Hong, Haojian Lu | 2026-07-15 | Scientific reports | 0 | 16 |
| visibility_off | Numerical Spectrum Linking: Identification of Governing PDE via Koopman-Chebyshev Approximation with Resampling | Phonepaserth Sisaykeo, S. Muramatsu | 2026-07-30 | ArXiv | 0 | 14 |
| visibility_off | Neural Discovery of Memory and Nonlocal Kernels in Integro-Differential Equations with Constrained Kolmogorov-Arnold Networks | Aruzhan Tleubek, Salah A. Faroughi | 2026-07-13 | ArXiv | 0 | 2 |
| visibility_off | Semi-analytical hierarchical Bayesian inference of nonlinear model structure in stochastic dynamics: Applied to compartmental models of infectious diseases | B. Robinson, Philippe Bisaillon, R. Sandhu, M. Khalil, J. Edwards, T. Kendzerska, Thomas Walker, Shirley Mills, C. Pettit, D. Poirel, A. Sarkar | 2026-07-10 | PLOS One | 0 | 30 |
| 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 | LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation | Jinyang Du, Hao Ma, Xiaohu Shi, Bo Yang, Yanchun Liang, Heow Pueh Lee, Chunguo Wu | 2026-07-12 | ArXiv | 1 | 19 |
| 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 | A Quantum Optimization Framework for Data-Assimilation-Augmented Parameter Estimation | Muhammad Jalil Ahmad, Mohammadhossein Mohammadisiahroudi, A. Biswas, Kathleen Hoffman | 2026-08-12 | ArXiv | 0 | 20 |
| visibility_off | Origins and mitigation of double descent in reduced order modeling | A. Klishin, J. Kutz, Krithika Manohar | 2026-07-29 | ArXiv | 1 | 15 |
| 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 | Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws | Aviral Prakash, M. Klasky | 2026-07-13 | ArXiv | 1 | 4 |
| visibility_off | Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection | Pongpisit Thanasutives, Yoshinobu Kawahara | 2026-08-13 | ArXiv | 0 | 11 |
| 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 | 17 |
| 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 | 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, Tianmin Zhang, A. Cammi, Xiang Wang | 2026-07-20 | ArXiv | 0 | 38 |
| visibility_off | Gaussian process learning with flow map refinement for parameter estimation in dynamical systems | Y. Hao, Dongwei Ye | 2026-08-23 | ArXiv | 0 | 13 |
| visibility_off | Decoding gene regulatory networks from single-cell RNA velocity | Lingqi Meng, Shiruo Wang | 2026-08-10 | ArXiv | 0 | 1 |
| 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 | M. 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, Anton van Beek | 2026-08-31 | ArXiv | 0 | 7 |
| visibility_off | A novel Data-Driven representation of Linear Systems based on Dynamic Mode Decomposition with Control to deal with noisy data | Francesco Giannini | 2026-07-01 | 2026 12th International Conference on Control, Decision and Information Technologies (CoDIT) | 0 | 5 |
| visibility_off | RTS Smoother-Guided Learning of Physics-Based Neural Differential Models | Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba, Zachary C. Danziger, Deniz Erdoğmuş | 2026-07-16 | ArXiv | 0 | 18 |
| visibility_off | System identification for complex dynamical systems: a survey | Xiaoyu Zhang, Zi-Qiang Li, Ruizhe Shi, Shiyu Wang, Feng Wang, Duxin Chen | 2026-07-16 | Complex Engineering Systems | 0 | 18 |
| 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 | Physics-Integrated Operator Learning via Gaussian Splatting Representations | Jihao Zhang, Junyi Guo, Jian-Xun Wang | 2026-08-25 | ArXiv | 0 | 6 |
| visibility_off | Learning piecewise-smooth dynamical systems | D. Murari, Erik Jansson, Chris Budd Obe, C. Schönlieb | 2026-08-20 | ArXiv | 0 | 51 |
| 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 | Personalized Growth Modeling Using Local Polynomials | Pratyusha Sarkar, Siddhartha Nandy, Snigdhansu Chatterjee | 2026-07-01 | 2026 IEEE International Conference on Digital Health (ICDH) | 0 | 18 |
| 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 | Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers | F. Faraji, Francesco Belardinelli | 2026-08-01 | ArXiv | 0 | 13 |
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |