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arXiv 提交日期: 2026-03-16
📄 Abstract - A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations

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.

顶级标签: systems agents model training
详细标签: stellarator coil optimization genetic algorithm llm agents finite-element analysis automated design 或 搜索:

一种结合在线有限元计算的自动化AI驱动仿星器线圈优化的概念验证 / A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations


1️⃣ 一句话总结

这篇论文提出了一种全自动化的AI驱动系统,能够利用遗传算法或大语言模型快速优化仿星器核聚变装置中复杂线圈的设计,并首次在优化循环中集成了线圈应力的在线有限元计算,从而大幅加速了反应堆的设计流程。

源自 arXiv: 2603.15240