Interpretable Scientific Discovery with Symbolic Regression: A Review
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866929672462270464 |
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| author | Makke, Nour Chawla, Sanjay |
| author_facet | Makke, Nour Chawla, Sanjay |
| contents | Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_10873 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Interpretable Scientific Discovery with Symbolic Regression: A Review Makke, Nour Chawla, Sanjay Machine Learning Artificial Intelligence High Energy Physics - Phenomenology Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations. |
| title | Interpretable Scientific Discovery with Symbolic Regression: A Review |
| topic | Machine Learning Artificial Intelligence High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2211.10873 |