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English(EN) Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS

人工智能驱动的虚拟电路增强聚变实验中的等离子体控制

研究人员开发并测试了一种使用神经网络模拟器控制托卡马克聚变反应堆中等离子体形状的新方法。该系统通过根据电流和线圈参数预测等离子体形状来与MAST-U等离子体控制系统(PCS)集成。该方法利用实时C++推理服务器提供形状预测和雅可比矩阵,从而能够计算虚拟电路矩阵和更新的线圈电流请求以进行精确驱动。该框架旨在增强对聚变系统基于AI的控制的信心,并直接应用于即将进行的MAST-U实验和未来的聚变装置。 AI

影响 这项研究展示了人工智能在增强聚变能源控制系统方面的实际应用,有望加速未来聚变装置的开发。

排序理由 详细介绍一种新的基于AI的聚变反应堆控制方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

人工智能驱动的虚拟电路增强聚变实验中的等离子体控制

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详细介绍一种新的基于AI的聚变反应堆控制方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Matthew J. Marshall, Edward Jones, Graham J. McArdle, Alasdair Ross, Kamran Pentland, Nicola C. Amorisco, Charles Vincent, Martin Kochan, Colin Hogben, Graham Jones, Adam Stephen, George K. Holt, Adriano Agnello ·

    用于等离子体形状控制的神经网络模拟器实时虚拟电路:在MAST-U PCS中的集成与测试

    arXiv:2608.26216v1 Announce Type: cross Abstract: The deployment of advanced, AI-enabled control algorithms in tokamak experiments requires robust integration with existing plasma control system (PCS) architectures and extensive pre-experimental validation. In this contribution, …