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Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
Machine learning offers an intriguing alternative to first-principle analysis for discovering new physics from experimental data. However, to date, purely data-driven methods have only proven successful in uncovering physical laws describing simple, low-dimensional systems with low levels of noise....
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163752/ https://www.ncbi.nlm.nih.gov/pubmed/34050155 http://dx.doi.org/10.1038/s41467-021-23479-0 |