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English(EN) Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks

AI模型从有限传感器数据中估算核反应堆状态 · 已追踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的浅层循环解码器（SHRED）网络，用于核反应堆的状态估计。这种深度学习架构仅使用有限的、有噪声的传感器数据即可推断出复杂的反应堆状态，包括中子通量和温度。SHRED网络已成功应用于第四代反应堆（如熔盐快堆）的理论模型以及已部署的TRIGA Mark II反应堆，展示了其在数字孪生应用中进行实时监控和控制的多功能性和潜力。 AI

影响 这项研究展示了人工智能通过先进的状态估计来提高核反应堆运行安全性和效率的潜力。

排序理由 该集群包含两篇详细介绍新颖人工智能模型在工程问题中应用的学术论文。

在 arXiv cs.LG 阅读 →

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AI模型从有限传感器数据中估算核反应堆状态 · 已追踪2个来源

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该集群包含两篇详细介绍新颖人工智能模型在工程问题中应用的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi ·

    面向循环燃料反应堆的高效参数化状态估计：采用浅层循环解码器网络

    arXiv:2503.08904v3 Announce Type: replace Abstract: The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent particularly challenging applications for this ta…

  2. arXiv cs.LG TIER_1 English(EN) · Stefano Riva, Carolina Introini, Jos\`e Nathan Kutz, Antonio Cammi ·

    基于TRIGA Mark II反应堆浅层循环解码器的受限传感与可靠状态估计

    arXiv:2510.12368v2 Announce Type: replace-cross Abstract: Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique desig…

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