Symbolic regression
This page was last updated on 2024-09-16 06:06:54 UTC
Manually curated articles on Symbolic regression
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
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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 | 3270 | 65 | 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 | 87 | 23 | 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 | 620 | 65 | 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 | 452 | 65 | 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 | 435 | 65 | 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 | 321 | 65 | 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 | 205 | 65 | 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 | 62 | 77 | 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 | 32 | 65 | 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 | 71 | 65 | 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 | 1196 | 65 | 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 | 164 | 65 | open_in_new |
visibility_off | Learning sparse nonlinear dynamics via mixed-integer optimization | D. Bertsimas, Wes Gurnee | 2022-06-01 | Nonlinear Dynamics | 29 | 91 | 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 | 116 | 65 | open_in_new |
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
Recommended articles on Symbolic regression
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visibility_off | Discovering Governing equations from Graph-Structured Data by Sparse Identification of Nonlinear Dynamical Systems | Mohammad Amin Basiri, Sina Khanmohammadi | 2024-09-02 | ArXiv | 0 | 3 |
visibility_off | Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems | Shane A. McQuarrie, Anirban Chaudhuri, Karen Willcox, Mengwu Guo | 2024-08-06 | ArXiv | 0 | 6 |
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visibility_off | BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo | M.D. Champneys, T. J. Rogers | 2024-08-15 | ArXiv | 0 | 1 |
visibility_off | Spectrally Informed Learning of Fluid Flows | Benjamin D. Shaffer, Jeremy R. Vorenberg, M. A. Hsieh | 2024-08-26 | ArXiv | 0 | 2 |
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visibility_off | Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics | Yenho Chen, Noga Mudrik, Kyle A. Johnsen, Sankaraleengam (Sankar) Alagapan, Adam S. Charles, Christopher J. Rozell | 2024-08-29 | ArXiv | 0 | 19 |
visibility_off | Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systems | James H. Adler, Samuel Hocking, Xiaozhe Hu, Shafiqul Islam | 2024-07-25 | ArXiv | 0 | 2 |
visibility_off | Learning Noise-Robust Stable Koopman Operator for Control with Physics-Informed Observables | Shahriar Akbar Sakib, Shaowu Pan | 2024-08-13 | ArXiv | 0 | 0 |
visibility_off | Accurate data‐driven surrogates of dynamical systems for forward propagation of uncertainty | Saibal De, Reese E. Jones, H. Kolla | 2024-08-03 | International Journal for Numerical Methods in Engineering | 0 | 30 |
visibility_off | Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery | Pongpisit Thanasutives, Ken-ichi Fukui | 2024-08-15 | ArXiv | 0 | 3 |
visibility_off | Stable Sparse Operator Inference for Nonlinear Structural Dynamics | P. D. Boef, Diana Manvelyan, Jos Maubach, W. Schilders, N. Wouw | 2024-07-31 | ArXiv | 0 | 47 |
visibility_off | Koopman Operators in Robot Learning | Lu Shi, Masih Haseli, Giorgos Mamakoukas, Daniel Bruder, Ian Abraham, Todd Murphey, Jorge Cortes, Konstantinos Karydis | 2024-08-08 | ArXiv | 0 | 9 |
visibility_off | Learning Latent Space Dynamics with Model-Form Uncertainties: A Stochastic Reduced-Order Modeling Approach | Jin Yi Yong, Rudy Geelen, Johann Guilleminot | 2024-08-30 | ArXiv | 0 | 1 |
visibility_off | Relaxation-based schemes for on-the-fly parameter estimation in dissipative dynamical systems | Vincent R. Martinez, Jacob Murri, J. Whitehead | 2024-08-26 | ArXiv | 0 | 1 |
visibility_off | Learning Global Linear Representations of Truly Nonlinear Dynamics | Thomas Breunung, F. Kogelbauer | 2024-08-06 | ArXiv | 0 | 6 |
visibility_off | A PINN approach for the online identification and control of unknown PDEs | Alessandro Alla, Giulia Bertaglia, Elisa Calzola | 2024-08-06 | ArXiv | 0 | 1 |
visibility_off | Sparse identification of time delay systems via pseudospectral collocation | Enrico Bozzo, Dimitri Breda, Muhammad Tanveer | 2024-08-04 | ArXiv | 0 | 0 |
visibility_off | Data-driven identification of latent port-Hamiltonian systems | J. Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, J. Fehr, B. Haasdonk | 2024-08-15 | ArXiv | 0 | 32 |
visibility_off | Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator | Xinghao Dong, Chuanqi Chen, Jin-Long Wu | 2024-08-06 | ArXiv | 1 | 2 |
visibility_off | Data-driven ODE modeling of the high-frequency complex dynamics of a fluid flow | Natsuki Tsutsumi, Kengo Nakai, Yoshitaka Saiki | 2024-09-01 | ArXiv | 0 | 4 |
visibility_off | Learning Stable Evolutionary PDE Dynamics: A Scalable System Identification Approach | Diyou Liu, Mohammad Khosravi | 2024-08-21 | 2024 IEEE Conference on Control Technology and Applications (CCTA) | 0 | 0 |
visibility_off | Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models | Matteo Tomasetto, Andrea Manzoni, Francesco Braghin | 2024-09-09 | ArXiv | 0 | 0 |
visibility_off | Sampling parameters of ordinary differential equations with Langevin dynamics that satisfy constraints | Chris Chi, J. Weare, Aaron R Dinner | 2024-08-28 | ArXiv | 0 | 20 |
visibility_off | Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems | Amber Hu, D. Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott W. Linderman | 2024-07-19 | ArXiv | 0 | 27 |
visibility_off | Higher order quantum reservoir computing for non-intrusive reduced-order models | Vinamr Jain, R. Maulik | 2024-07-31 | ArXiv | 0 | 22 |
visibility_off | On latent dynamics learning in nonlinear reduced order modeling | N. Farenga, S. Fresca, Simone Brivio, A. Manzoni | 2024-08-27 | ArXiv | 0 | 11 |
visibility_off | Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators | Chuwei Wang, Julius Berner, Zong-Yi Li, Di Zhou, Jiayun Wang, Jane Bae, A. Anandkumar | 2024-08-09 | ArXiv | 0 | 18 |
visibility_off | Predicting multi-parametric dynamics of externally forced oscillators using reservoir computing and minimal data | Manish Yadav, Swati Chauhan, M. Shrimali, M. Stender | 2024-08-27 | ArXiv | 0 | 19 |
visibility_off | Enhancing spectral analysis in nonlinear dynamics with pseudoeigenfunctions from continuous spectra | Itsushi Sakata, Yoshinobu Kawahara | 2024-08-20 | Scientific Reports | 0 | 8 |
visibility_off | Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data | Phillip Si, Peng Chen | 2024-08-29 | ArXiv | 0 | 0 |
visibility_off | Stochastic Neural Simulator for Generalizing Dynamical Systems across Environments | Liu Jiaqi, Jiaxu Cui, Jiayi Yang, Bo Yang | 2024-08-01 | Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence | 0 | 5 |
visibility_off | Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling | Mingxue Yan, Minghao Han, A. Law, Xunyuan Yin | 2024-08-07 | ArXiv | 0 | 35 |
visibility_off | Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems | Matteo Caldana, J. Hesthaven | 2024-08-12 | ArXiv | 0 | 64 |
visibility_off | Optimal Experimental Design for Universal Differential Equations | Christoph Plate, Carl Julius Martensen, Sebastian Sager | 2024-08-13 | ArXiv | 0 | 1 |
visibility_off | State Space Kriging model for emulating complex nonlinear dynamical systems under stochastic excitation | Kai Chenga, Iason Papaioannoua, MengZe Lyub, Daniel Straub | 2024-09-04 | ArXiv | 0 | 0 |
visibility_off | A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations | A. Aghaei | 2024-09-05 | ArXiv | 0 | 5 |
visibility_off | Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems | Félix Chavelli, Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low, Stéphane Bressan | 2024-08-21 | ArXiv | 0 | 3 |
visibility_off | Practical Guidelines for Data-driven Identification of Lifted Linear Predictors for Control | Loi Do, Adam Uchytil, Zdenvek Hur'ak | 2024-08-02 | ArXiv | 1 | 2 |
visibility_off | Model free data assimilation with Takens embedding | Ziyi Wang, Lijian Jiang | 2024-08-16 | ArXiv | 0 | 0 |
visibility_off | Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach | Daniel Lengyel, P. Parpas, B. Hamzi, H. Owhadi | 2024-08-12 | ArXiv | 0 | 37 |
visibility_off | 4D-Var using Hessian approximation and backpropagation applied to automatically-differentiable numerical and machine learning models | Kylen Solvik, Stephen G. Penny, Stephan Hoyer | 2024-08-05 | ArXiv | 0 | 10 |
visibility_off | Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems | Yuan Chen, Dongbin Xiu | 2024-08-27 | ArXiv | 0 | 1 |
Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |