Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912020784218112 |
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| author | Bethel, E. Wes Cramer, Vianna del Rio, Alexander Narins, Lothar Pestano, Chris Verma, Satvik Arias, Erick Bertelli, Nicola Perciano, Talita Shiraiwa, Syun'ichi Villar, Álvaro Sánchez Wallace, Greg Wright, John C. |
| author_facet | Bethel, E. Wes Cramer, Vianna del Rio, Alexander Narins, Lothar Pestano, Chris Verma, Satvik Arias, Erick Bertelli, Nicola Perciano, Talita Shiraiwa, Syun'ichi Villar, Álvaro Sánchez Wallace, Greg Wright, John C. |
| contents | This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science Bethel, E. Wes Cramer, Vianna del Rio, Alexander Narins, Lothar Pestano, Chris Verma, Satvik Arias, Erick Bertelli, Nicola Perciano, Talita Shiraiwa, Syun'ichi Villar, Álvaro Sánchez Wallace, Greg Wright, John C. Artificial Intelligence Software Engineering This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models. |
| title | Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science |
| topic | Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2409.06122 |