Skip to content

This page was last updated on 2026-02-09 06:34:25 UTC

Recommendations for the article Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index
visibility_off Automated discovery of fundamental variables hidden in experimental data Boyuan Chen, Kuang Huang, Sunand Raghupathi, I. Chandratreya, Qi Du, H. Lipson 2022-07-01 Nature Computational Science 120 21
visibility_off DeepMoD: Deep learning for model discovery in noisy data G. Both, Subham Choudhury, P. Sens, R. Kusters 2019-04-20 J. Comput. Phys. 133 37
visibility_off Discovering sparse interpretable dynamics from partial observations Peter Y. Lu, Joan Ariño Bernad, M. Soljačić 2021-07-22 Communications Physics 36 98
visibility_off PNAS Plus Significance Statements Ronald R Coifman, David A. Kessler, A. Goodkind 2017-09-19 Proceedings of the National Academy of Sciences 39 13
visibility_off Discovering State Variables Hidden in Experimental Data Boyuan Chen, Kuang Huang, Sunand Raghupathi, I. Chandratreya, Qi Du, Hod Lipson 2021-12-20 ArXiv 19 74
visibility_off Hierarchical Physics-Embedded Learning for Prediction and Discovery in Spatiotemporal Dynamical Systems Xizhe Wang, Xiaobin Song, Qingshan Jia, Hao Sun, Hongbo Zhao, Benben Jiang 2025-10-29 ArXiv 0 3
visibility_off A physics-informed operator regression framework for extracting data-driven continuum models Ravi G. Patel, N. Trask, M. Wood, E. Cyr 2020-09-25 ArXiv 120 19
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index