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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.24577 |
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| _version_ | 1866908624311287808 |
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| author | Yang, He Ren, Fei Calabro, Francesco Yu, Hai-Sui Chen, Xiaohui Zhuang, Pei-Zhi |
| author_facet | Yang, He Ren, Fei Calabro, Francesco Yu, Hai-Sui Chen, Xiaohui Zhuang, Pei-Zhi |
| contents | We are delighted to see the recent development of physics-informed extreme learning machine (PIELM) for its higher computational efficiency and accuracy compared to other physics-informed machine learning (PIML) paradigms. Since a comprehensive summary or review of PIELM is currently unavailable, we would like to take this opportunity to share our perspectives and experiences on this promising research direction. We can see that many efforts have been made to solve ordinary/partial differential equations (ODEs/PDEs) characterized by sharp gradients, nonlinearities, high-frequency behavior, hard constraints, uncertainty, multiphysics coupling, and interpretability. Despite these encouraging successes, many pressing challenges remain to be tackled, which also provides opportunities to develop more robust, interpretable, and generalizable PIELM frameworks for scientific and engineering applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges Yang, He Ren, Fei Calabro, Francesco Yu, Hai-Sui Chen, Xiaohui Zhuang, Pei-Zhi Machine Learning We are delighted to see the recent development of physics-informed extreme learning machine (PIELM) for its higher computational efficiency and accuracy compared to other physics-informed machine learning (PIML) paradigms. Since a comprehensive summary or review of PIELM is currently unavailable, we would like to take this opportunity to share our perspectives and experiences on this promising research direction. We can see that many efforts have been made to solve ordinary/partial differential equations (ODEs/PDEs) characterized by sharp gradients, nonlinearities, high-frequency behavior, hard constraints, uncertainty, multiphysics coupling, and interpretability. Despite these encouraging successes, many pressing challenges remain to be tackled, which also provides opportunities to develop more robust, interpretable, and generalizable PIELM frameworks for scientific and engineering applications. |
| title | Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.24577 |