Symbolic regression

This page was last updated on 2025-03-03 06:06:34 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 3591 68 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 97 24 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 681 68 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 495 68 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 488 68 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 343 68 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 238 68 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 67 80 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 38 68 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 79 68 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 197 68 open_in_new
visibility_off Learning sparse nonlinear dynamics via mixed-integer optimization D. Bertsimas, Wes Gurnee 2022-06-01 Nonlinear Dynamics 33 93 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 129 68 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 Impilict Runge-Kutta based sparse identification of governing equations in biologically motivated systems Mehrdad Anvari, H. Marasi, Hossein Kheiri 2025-02-27 ArXiv 0 2
visibility_off Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Matthew Golden, K. Satapathy, D. Psaltis 2025-01-07 ArXiv 0 62
visibility_off A Bayesian Approach for Discovering Time- Delayed Differential Equation from Data Debangshu Chowdhury, Souvik Chakraborty 2025-01-06 ArXiv 0 1
visibility_off Mechanistic PDE Networks for Discovery of Governing Equations Adeel Pervez, E. Gavves, Francesco Locatello 2025-02-25 ArXiv 0 37
visibility_off Physics-informed Split Koopman Operators for Data-efficient Soft Robotic Simulation Eron Ristich, Lei Zhang, Yi Ren, Jiefeng Sun 2025-01-31 ArXiv 0 2
visibility_off Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application Jonah Botvinick-Greenhouse 2025-01-31 ArXiv 0 3
visibility_off Al-Khwarizmi: Discovering Physical Laws with Foundation Models Christopher E. Mower, Haitham Bou-Ammar 2025-02-03 ArXiv 0 26
visibility_off Physics-Aware Sparse Signal Recovery Through PDE-Governed Measurement Systems Tadashi Wadayama, Koji Igarashi, Takumi Takahashi 2025-01-23 ArXiv 1 1
visibility_off Characterizing nonlinear dynamics by contrastive cartography Nicolas Romeo, Chris Chi, Aaron R. Dinner, Elizabeth R. Jerison 2025-01-30 ArXiv 0 3
visibility_off Data-driven system identification using quadratic embeddings of nonlinear dynamics Stefan Klus, J. N'konzi 2025-01-14 ArXiv 0 2
visibility_off EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems Jianhong Chen, Shihao Yang 2025-01-23 ArXiv 0 0
visibility_off Non-intrusive reduced-order modeling for dynamical systems with spatially localized features L. Gkimisis, Nicole Aretz, Marco Tezzele, Thomas Richter, Peter Benner, Karen E. Willcox 2025-01-08 ArXiv 0 3
visibility_off Stable Port-Hamiltonian Neural Networks Fabian J. Roth, D. K. Klein, Maximilian Kannapinn, Jan Peters, Oliver Weeger 2025-02-04 ArXiv 0 6
visibility_off Derivative-Free Domain-Informed Data-Driven Discovery of Sparse Kinetic Models Siddharth Prabhu, Nick Kosir, M. Kothare, Srinivas Rangarajan 2025-01-27 Industrial & Engineering Chemistry Research 0 32
visibility_off Nonlinear port-Hamiltonian system identification from input-state-output data Karim Cherifi, Achraf El Messaoudi, Hannes Gernandt, Marco Roschkowski 2025-01-10 ArXiv 0 8
visibility_off Neural equilibria for long-term prediction of nonlinear conservation laws Jose Antonio Lara Benitez, Junyi Guo, Kareem Hegazy, Ivan Dokmanic, Michael W. Mahoney, Maarten V. de Hoop 2025-01-12 ArXiv 0 6
visibility_off Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks Zhiwei Gao, G. Karniadakis 2025-01-15 ArXiv 0 132
visibility_off Deep Operator Networks for Bayesian Parameter Estimation in PDEs Amogh Raj, Carol Eunice Gudumotou, Sakol Bun, Keerthana Srinivasa, Arash Sarshar 2025-01-18 ArXiv 0 0
visibility_off Controlling Transient Chaos in the Lorenz System with Machine Learning David Valle, Rubén Capeáns, Alexandre Wagemakers, M.A.F. Sanju'an 2025-01-29 ArXiv 0 3
visibility_off High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator Biao Yuan, He Wang, Yanjie Song, Ana Heitor, Xiaohui Chen 2025-02-26 ArXiv 0 2
visibility_off Physics-Informed Neuro-Evolution (PINE): A Survey and Prospects Jian Cheng Wong, Abhishek Gupta, Chin Chun Ooi, P. Chiu, Jiao Liu, Y. Ong 2025-01-11 ArXiv 0 14
visibility_off From disorganized data to emergent dynamic models: Questionnaires to partial differential equations David W Sroczynski, Felix P. Kemeth, A. Georgiou, Ronald R Coifman, I. Kevrekidis 2025-01-21 PNAS Nexus 0 8
visibility_off Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods J. Jordanou, Eduardo Camponogara, Eduardo Gildin 2025-02-04 ArXiv 0 7
visibility_off Data-Driven Reduced-Order Models for Port-Hamiltonian Systems with Operator Inference Yuwei Geng, Lili Ju, Boris Kramer, Zhu Wang 2025-01-04 ArXiv 0 3
visibility_off Principled model selection for stochastic dynamics Andonis Gerardos, P. Ronceray 2025-01-17 ArXiv 0 16
visibility_off Training Neural ODEs Using Fully Discretized Simultaneous Optimization Mariia Shapovalova, Calvin Tsay 2025-02-21 ArXiv 0 0
visibility_off Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations Haoyang Zheng, Guang Lin 2025-02-01 ArXiv 0 2
visibility_off Machine Learning for Sparse Nonlinear Modeling and Control S. Brunton, Nicholas Zolman, J. Kutz, Urban Fasel 2025-01-14 Annual Review of Control, Robotics, and Autonomous Systems 1 68
visibility_off End-to-End Learning Framework for Solving Non-Markovian Optimal Control Xiaole Zhang, Peiyu Zhang, Xiongye Xiao, Shixuan Li, Vasileios Tzoumas, Vijay Gupta, Paul Bogdan 2025-02-07 ArXiv 0 19
visibility_off Discovering Polynomial and Quadratic Structure in Nonlinear Ordinary Differential Equations Boris Kramer, G. Pogudin 2025-02-14 ArXiv 0 12
visibility_off Polynomial Optimization for Nonlinear Dynamics: Theory, Algorithms and Applications Giovanni Fantuzzi, D. Goluskin, Jean-Bernard Lasserre 2025-02-14 Oberwolfach Reports 0 15
visibility_off No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs Krzysztof Kacprzyk, M. Schaar 2025-01-30 ArXiv 0 66
visibility_off Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling Kian P. Abdolazizi, R. Aydin, C. Cyron, K. Linka 2025-02-08 ArXiv 0 30
visibility_off Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks Mars Liyao Gao, Jan P. Williams, J. Kutz 2025-01-23 ArXiv 0 33
visibility_off Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Girnar Goyal, Philipp Holl, Sweta Agrawal, Nils Thuerey 2025-01-27 ArXiv 0 8
visibility_off On the importance of structural identifiability for machine learning with partially observed dynamical systems Janis Norden, Elisa Oostwal, Michael Chappell, Peter Tiño, K. Bunte 2025-02-06 ArXiv 0 2
visibility_off Toward a physics-guided machine learning approach for predicting chaotic systems dynamics Liu Feng, Yang Liu, Benyun Shi, Jiming Liu 2025-01-17 Frontiers in Big Data 0 2
visibility_off Flow-based linear embedding for Bayesian filtering of nonlinear stochastic dynamical systems Xintong Wang, Xiaofei Guan, Ling Guo, Hao Wu 2025-02-22 ArXiv 0 1
visibility_off A Comparison of Strategies to Embed Physics-Informed Neural Networks in Nonlinear Model Predictive Control Formulations Solved via Direct Transcription Carlos Andr'es Elorza Casas, Luis A. Ricardez-Sandoval, J. Pulsipher 2025-01-10 ArXiv 0 6
visibility_off Sparse Identification for bifurcating phenomena in Computational Fluid Dynamics Lorenzo Tomada, M. Khamlich, F. Pichi, G. Rozza 2025-02-16 ArXiv 0 51
visibility_off Data-driven Control of T-Product-based Dynamical Systems Ziqin He, Yidan Mei, Shenghan Mei, Xin Mao, Anqi Dong, Ren Wang, Can Chen 2025-02-20 ArXiv 0 1
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index