ABSTRACT

The central magnetic flux density is usually simulated via finite element methods that require a significant amount of inputs and computation resources. We develop the Gaussian process regression (GPR) model to shed light on the statistical relationship between structural descriptors of the iron yoke and the central magnetic flux density for superconducting solenoids. The model is highly stable and accurate, contributing to fast and robust estimations of the central magnetic flux density.

中文翻译：

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机器学习超导螺线管的中心磁通密度

摘要

中心磁通密度通常通过需要大量输入和计算资源的有限元方法来模拟。我们开发了高斯过程回归 (GPR) 模型，以阐明铁轭的结构描述符与超导螺线管的中心磁通密度之间的统计关系。该模型高度稳定和准确，有助于快速、稳健地估计中心磁通密度。