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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.04001 |
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| _version_ | 1866909889673035776 |
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| author | Kutz, J. Nathan Battaglia, Peter Brenner, Michael Carlberg, Kevin Hagberg, Aric Ho, Shirley Hoyer, Stephan Lange, Henning Lipson, Hod Mahoney, Michael W. Noe, Frank Welling, Max Zanna, Laure Zhu, Francis Brunton, Steven L. |
| author_facet | Kutz, J. Nathan Battaglia, Peter Brenner, Michael Carlberg, Kevin Hagberg, Aric Ho, Shirley Hoyer, Stephan Lange, Henning Lipson, Hod Mahoney, Michael W. Noe, Frank Welling, Max Zanna, Laure Zhu, Francis Brunton, Steven L. |
| contents | Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical, and biological sciences. These emerging modeling paradigms require comparative metrics to evaluate a diverse set of scientific objectives, including forecasting, state reconstruction, generalization, and control, while also considering limited data scenarios and noisy measurements. We introduce a common task framework (CTF) for science and engineering, which features a growing collection of challenge data sets with a diverse set of practical and common objectives. The CTF is a critically enabling technology that has contributed to the rapid advance of ML/AI algorithms in traditional applications such as speech recognition, language processing, and computer vision. There is a critical need for the objective metrics of a CTF to compare the diverse algorithms being rapidly developed and deployed in practice today across science and engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating scientific discovery with the common task framework Kutz, J. Nathan Battaglia, Peter Brenner, Michael Carlberg, Kevin Hagberg, Aric Ho, Shirley Hoyer, Stephan Lange, Henning Lipson, Hod Mahoney, Michael W. Noe, Frank Welling, Max Zanna, Laure Zhu, Francis Brunton, Steven L. Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical, and biological sciences. These emerging modeling paradigms require comparative metrics to evaluate a diverse set of scientific objectives, including forecasting, state reconstruction, generalization, and control, while also considering limited data scenarios and noisy measurements. We introduce a common task framework (CTF) for science and engineering, which features a growing collection of challenge data sets with a diverse set of practical and common objectives. The CTF is a critically enabling technology that has contributed to the rapid advance of ML/AI algorithms in traditional applications such as speech recognition, language processing, and computer vision. There is a critical need for the objective metrics of a CTF to compare the diverse algorithms being rapidly developed and deployed in practice today across science and engineering. |
| title | Accelerating scientific discovery with the common task framework |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2511.04001 |