This page was last updated on 2025-06-23 11:22:55 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 | 94 | 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. | 116 | 37 |
| visibility_off | Discovering sparse interpretable dynamics from partial observations | Peter Y. Lu, Joan Ariño Bernad, M. Soljačić | 2021-07-22 | Communications Physics | 25 | 96 |
| 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 | 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 | 104 | 18 |
| visibility_off | Data-Driven Discovery of Coarse-Grained Equations | Joseph Bakarji, D. Tartakovsky | 2020-01-30 | J. Comput. Phys. | 32 | 47 |
| visibility_off | Universal Differential Equations for Scientific Machine Learning | Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, K. Zubov, R. Supekar, Dominic J. Skinner, A. Ramadhan | 2020-01-13 | ArXiv | 597 | 26 |
| visibility_off | Physics-informed learning of governing equations from scarce data | Zhao Chen, Yang Liu, Hao Sun | 2020-05-05 | Nature Communications | 396 | 14 |
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |