Discovering physical laws with parallel symbolic enumeration
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914032241344512 |
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| author | Ruan, Kai Xu, Yilong Gao, Ze-Feng Guo, Yike Sun, Hao Wen, Ji-Rong Liu, Yang |
| author_facet | Ruan, Kai Xu, Yilong Gao, Ze-Feng Guo, Yike Sun, Hao Wen, Ji-Rong Liu, Yang |
| contents | Symbolic regression plays a crucial role in modern scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data. A key challenge lies in the search for parsimonious and generalizable mathematical formulas, in an infinite search space, while intending to fit the training data. Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity, which essentially hinders the pace of applying symbolic regression for scientific exploration across interdisciplinary domains. To this end, we introduce parallel symbolic enumeration (PSE) to efficiently distill generic mathematical expressions from limited data. Experiments show that PSE achieves higher accuracy and faster computation compared to the state-of-the-art baseline algorithms across over 200 synthetic and experimental problem sets (e.g., improving the recovery accuracy by up to 99% and reducing runtime by an order of magnitude). PSE represents an advance in accurate and efficient data-driven discovery of symbolic, interpretable models (e.g., underlying physical laws), and improves the scalability of symbolic learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Discovering physical laws with parallel symbolic enumeration Ruan, Kai Xu, Yilong Gao, Ze-Feng Guo, Yike Sun, Hao Wen, Ji-Rong Liu, Yang Machine Learning Artificial Intelligence I.2 Symbolic regression plays a crucial role in modern scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data. A key challenge lies in the search for parsimonious and generalizable mathematical formulas, in an infinite search space, while intending to fit the training data. Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity, which essentially hinders the pace of applying symbolic regression for scientific exploration across interdisciplinary domains. To this end, we introduce parallel symbolic enumeration (PSE) to efficiently distill generic mathematical expressions from limited data. Experiments show that PSE achieves higher accuracy and faster computation compared to the state-of-the-art baseline algorithms across over 200 synthetic and experimental problem sets (e.g., improving the recovery accuracy by up to 99% and reducing runtime by an order of magnitude). PSE represents an advance in accurate and efficient data-driven discovery of symbolic, interpretable models (e.g., underlying physical laws), and improves the scalability of symbolic learning. |
| title | Discovering physical laws with parallel symbolic enumeration |
| topic | Machine Learning Artificial Intelligence I.2 |
| url | https://arxiv.org/abs/2407.04405 |