Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912293239914496 |
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| author | Oktavian, M. Rizki Tunga, Anirudh Bakshi, Amandeep Mueterthies, Michael J. Gruenwald, J. Thomas Nistor, Jonathan |
| author_facet | Oktavian, M. Rizki Tunga, Anirudh Bakshi, Amandeep Mueterthies, Michael J. Gruenwald, J. Thomas Nistor, Jonathan |
| contents | The optimization of nuclear engineering designs, such as nuclear fuel assembly configurations, involves managing competing objectives like reactivity control and power distribution. This study explores the use of Optimization by Prompting, an iterative approach utilizing large language models (LLMs), to address these challenges. The method is straightforward to implement, requiring no hyperparameter tuning or complex mathematical formulations. Optimization problems can be described in plain English, with only an evaluator and a parsing script needed for execution. The in-context learning capabilities of LLMs enable them to understand problem nuances, therefore, they have the potential to surpass traditional metaheuristic optimization methods. This study demonstrates the application of LLMs as optimizers to Boiling Water Reactor (BWR) fuel lattice design, showing the capability of commercial LLMs to achieve superior optimization results compared to traditional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems Oktavian, M. Rizki Tunga, Anirudh Bakshi, Amandeep Mueterthies, Michael J. Gruenwald, J. Thomas Nistor, Jonathan Machine Learning Computational Physics The optimization of nuclear engineering designs, such as nuclear fuel assembly configurations, involves managing competing objectives like reactivity control and power distribution. This study explores the use of Optimization by Prompting, an iterative approach utilizing large language models (LLMs), to address these challenges. The method is straightforward to implement, requiring no hyperparameter tuning or complex mathematical formulations. Optimization problems can be described in plain English, with only an evaluator and a parsing script needed for execution. The in-context learning capabilities of LLMs enable them to understand problem nuances, therefore, they have the potential to surpass traditional metaheuristic optimization methods. This study demonstrates the application of LLMs as optimizers to Boiling Water Reactor (BWR) fuel lattice design, showing the capability of commercial LLMs to achieve superior optimization results compared to traditional methods. |
| title | Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2503.19620 |