A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations
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| Format: | Preprint |
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2026
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| _version_ | 1866908889323143168 |
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| author | Kaptanoglu, Alan A. Gil, Pedro F. |
| author_facet | Kaptanoglu, Alan A. Gil, Pedro F. |
| contents | Finding feasible coils for stellarator fusion devices is a critical challenge of realizing this concept for future power plants. Years of research work can be put into the design of even a single reactor-scale stellarator design. To rapidly speed up and automate the workflow of designing stellarator coils, we have designed an end-to-end ``runner'' for performing stellarator coil optimization. The entirety of pre and post-processing steps have been automated; the user specifies only a few basic input parameters, and final coil solutions are updated on an open-source leaderboard. Two policies are available for performing non-stop automated coil optimizations through a genetic algorithm or a context-aware LLM. Lastly, we construct a novel in-the-loop optimization of Von Mises stresses in the coils, opening up important future capabilities for in-the-loop finite-element calculations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15240 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations Kaptanoglu, Alan A. Gil, Pedro F. Plasma Physics Machine Learning Computational Physics Finding feasible coils for stellarator fusion devices is a critical challenge of realizing this concept for future power plants. Years of research work can be put into the design of even a single reactor-scale stellarator design. To rapidly speed up and automate the workflow of designing stellarator coils, we have designed an end-to-end ``runner'' for performing stellarator coil optimization. The entirety of pre and post-processing steps have been automated; the user specifies only a few basic input parameters, and final coil solutions are updated on an open-source leaderboard. Two policies are available for performing non-stop automated coil optimizations through a genetic algorithm or a context-aware LLM. Lastly, we construct a novel in-the-loop optimization of Von Mises stresses in the coils, opening up important future capabilities for in-the-loop finite-element calculations. |
| title | A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations |
| topic | Plasma Physics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2603.15240 |