时间推断 正确直角分解 (POD) 和辐射基函数 (RBF) 替代品在多热源电子设备中的短暂热场的一般化
Wenjun Zhao1,2, Bo Zhang1
1School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China.
Micromachines
|November 27, 2025
概括
一个新的减少顺序替代模型结合了正确的直角分解 (POD) 和辐射基函数 (RBF) 神经网络,用于在电子中准确的短暂温度预测. 这种方法显著降低了热管理的计算成本.
科学领域:
- 计算科学与工程 计算科学与工程
- 热分析是一种热分析.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确的过渡温度预测对于电子设备的有效热管理至关重要.
- 现有的方法可能在计算上昂贵,限制实时应用.
- 电子包装通常涉及来自多个热源的复杂热相互作用.
研究的目的:
- 为短暂温度场开发一种高效准确的减少顺序替代模型方法.
- 为了实现超越训练数据地平线的强有力的时间推断.
- 为了降低与电子系统中的热分析相关的计算成本.
主要方法:
- 使用正确的直角分解 (POD) 来减少维度.
- 采用辐射基函数 (RBF) 神经网络用于代用模型.
- 在参数-时间空间中映射时间条件模态系数.
主要成果:
- 拟议的POD-RBF替代模型实现了高预测准确度,全球MRE低于3%.
- 与传统方法相比,计算成本显著降低.
- 使用电子包的典型多热源传导模型验证了该方法.
结论:
- POD-RBF代用建模方法为短暂温度预测提供了高效和准确的解决方案.
- 该方法显示了电子系统实时热监测和管理的巨大潜力.
- 强大的时间推断能力提高了它对动态热场景的适用性.
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