A Perspective on Symbolic Machine Learning in Physical Sciences
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909510420922368 |
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| author | Makke, Nour Chawla, Sanjay |
| author_facet | Makke, Nour Chawla, Sanjay |
| contents | Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17993 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Perspective on Symbolic Machine Learning in Physical Sciences Makke, Nour Chawla, Sanjay Machine Learning High Energy Physics - Phenomenology High Energy Physics - Theory Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research. |
| title | A Perspective on Symbolic Machine Learning in Physical Sciences |
| topic | Machine Learning High Energy Physics - Phenomenology High Energy Physics - Theory |
| url | https://arxiv.org/abs/2502.17993 |