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深度神经操作员驱动的实时推断,使核能系统的数字双胞胎解决方案成为可能
Kazuma Kobayashi1, Syed Bahauddin Alam2,3
1Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-Champaign, Suite 100 Talbot Laboratory, 104 South Wright Street, Urbana, IL, 61801, USA.
Scientific reports
|January 24, 2024
概括
深度神经运营商网络 (DeepONet) 为核能系统中实时数字双胞胎 (DT) 推断提供了强大的解决方案. 这种方法减少了再培训需求,使复杂问题的预测更快,更准确.
科学领域:
- 核工程 核工程是指核工程.
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 核能领域的数字双胞胎 (DTs) 需要对各种运行条件进行实时预测能力.
- 传统的机器学习 (ML) 方法通常需要广泛的再培训,阻碍实时的DT应用.
- 深度神经运营者网络 (DeepONet) 为强大的代用模型提供了一个潜在的替代方案.
研究的目的:
- 评估DeepONet作为核能数字双胞胎的替代建模技术的可行性和性能.
- 评估DeepONet适用于实时推断的适用性,而无需在不同的反应堆条件下持续重新训练.
- 将DeepONet与传统的ML方法对核系统中的粒子传输问题进行基准测试.
主要方法:
- DeepONet使用一系列运行条件进行训练,以实现通用预测.
- 通过对传统的ML算法进行基准测试来评估性能.
- 该研究重点关注与核能相关的具有挑战性的粒子传输问题.
主要成果:
- DeepONet展示了高预测准确度和速度,超过了传统的ML方法.
- 该模型显示了可通用性和计算效率,适用于DT组件.
- 证实了DeepONet在没有不断的再培训的情况下进行实时推断的能力.
结论:
- DeepONet 是一个可行的和高效的替代模型工具,用于核能数字双胞胎的实时推断.
- 在最佳传感器放置和模型评估方面的挑战需要进一步研究以实现实际实施.
- 这项研究推进了DeepONet的使用,以提高核系统中的DT能力.
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