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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.14620 |
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| _version_ | 1866909697669332992 |
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| author | Wei, Shu Li, Yanjie Yu, Lina Li, Weijun Wu, Min Sun, Linjun Liu, Jingyi Qin, Hong Deng, Yusong Han, Jufeng Pang, Yan |
| author_facet | Wei, Shu Li, Yanjie Yu, Lina Li, Weijun Wu, Min Sun, Linjun Liu, Jingyi Qin, Hong Deng, Yusong Han, Jufeng Pang, Yan |
| contents | The quest for analytical solutions to differential equations has traditionally been constrained by the need for extensive mathematical expertise. Machine learning methods like genetic algorithms have shown promise in this domain, but are hindered by significant computational time and the complexity of their derived solutions. This paper introduces SSDE (Symbolic Solver for Differential Equations), a novel reinforcement learning-based approach that derives symbolic closed-form solutions for various differential equations. Evaluations across a diverse set of ordinary and partial differential equations demonstrate that SSDE outperforms existing machine learning methods, delivering superior accuracy and efficiency in obtaining analytical solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Closed-form Solutions: A New Perspective on Solving Differential Equations Wei, Shu Li, Yanjie Yu, Lina Li, Weijun Wu, Min Sun, Linjun Liu, Jingyi Qin, Hong Deng, Yusong Han, Jufeng Pang, Yan Machine Learning The quest for analytical solutions to differential equations has traditionally been constrained by the need for extensive mathematical expertise. Machine learning methods like genetic algorithms have shown promise in this domain, but are hindered by significant computational time and the complexity of their derived solutions. This paper introduces SSDE (Symbolic Solver for Differential Equations), a novel reinforcement learning-based approach that derives symbolic closed-form solutions for various differential equations. Evaluations across a diverse set of ordinary and partial differential equations demonstrate that SSDE outperforms existing machine learning methods, delivering superior accuracy and efficiency in obtaining analytical solutions. |
| title | Closed-form Solutions: A New Perspective on Solving Differential Equations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.14620 |