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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | https://arxiv.org/abs/2410.19843 |
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| _version_ | 1866918404642832384 |
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| author | Wang, Yizheng Bai, Jinshuai Lin, Zhongya Wang, Qimin Anitescu, Cosmin Sun, Jia Eshaghi, Mohammad Sadegh Gu, Yuantong Feng, Xi-Qiao Zhuang, Xiaoying Rabczuk, Timon Liu, Yinghua |
| author_facet | Wang, Yizheng Bai, Jinshuai Lin, Zhongya Wang, Qimin Anitescu, Cosmin Sun, Jia Eshaghi, Mohammad Sadegh Gu, Yuantong Feng, Xi-Qiao Zhuang, Xiaoying Rabczuk, Timon Liu, Yinghua |
| contents | In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve partial differential equations (AI for PDEs: Artificial intelligence for partial differential equations) has become a focal point in computational mechanics. The core of AI for PDEs is the fusion of data and partial differential equations (PDEs), which can solve almost any PDEs. Therefore, this article provides a comprehensive review of the research on AI for PDEs, summarizing the existing algorithms and theories. The article discusses the applications of AI for PDEs in computational mechanics, including solid mechanics, fluid mechanics, and biomechanics. The existing AI for PDEs algorithms include those based on Physics-Informed Neural Networks (PINNs), Deep Energy Methods (DEM), Operator Learning, and Physics-Informed Neural Operator (PINO). AI for PDEs represents a new method of scientific simulation that provides approximate solutions to specific problems using large amounts of data, then fine-tuning according to specific physical laws, avoiding the need to compute from scratch like traditional algorithms. Thus, AI for PDEs is the prototype for future foundation models in computational mechanics, capable of significantly accelerating traditional numerical algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial intelligence for partial differential equations in computational mechanics: A review Wang, Yizheng Bai, Jinshuai Lin, Zhongya Wang, Qimin Anitescu, Cosmin Sun, Jia Eshaghi, Mohammad Sadegh Gu, Yuantong Feng, Xi-Qiao Zhuang, Xiaoying Rabczuk, Timon Liu, Yinghua Systems and Control Machine Learning In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve partial differential equations (AI for PDEs: Artificial intelligence for partial differential equations) has become a focal point in computational mechanics. The core of AI for PDEs is the fusion of data and partial differential equations (PDEs), which can solve almost any PDEs. Therefore, this article provides a comprehensive review of the research on AI for PDEs, summarizing the existing algorithms and theories. The article discusses the applications of AI for PDEs in computational mechanics, including solid mechanics, fluid mechanics, and biomechanics. The existing AI for PDEs algorithms include those based on Physics-Informed Neural Networks (PINNs), Deep Energy Methods (DEM), Operator Learning, and Physics-Informed Neural Operator (PINO). AI for PDEs represents a new method of scientific simulation that provides approximate solutions to specific problems using large amounts of data, then fine-tuning according to specific physical laws, avoiding the need to compute from scratch like traditional algorithms. Thus, AI for PDEs is the prototype for future foundation models in computational mechanics, capable of significantly accelerating traditional numerical algorithms. |
| title | Artificial intelligence for partial differential equations in computational mechanics: A review |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2410.19843 |