集成BNN-LHS替代模型和热力学-EM分析用于COMSOL中高频低通波器的增强特征
Jorge Davalos-Guzman1, Jose L Chavez-Hurtado2, Zabdiel Brito-Brito3
1Intel Corporation, Folsom, CA 95630, USA.
Micromachines
|May 25, 2024
概括
本研究引入了一种使用贝叶斯神经网络 (BNNs) 和热力学建模的新方法来分析电磁系统. 它通过考虑现实世界的操作因素来改进电子元件设计,从而改善高频过器预测.
科学领域:
- 电磁学 电磁学 电磁学 电磁学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 传统的电磁特征描述往往忽略了多物理现象.
- 物理尺寸和操作条件的变化会影响电子元件的性能.
- 高频过器的准确建模需要整合各种物理效应.
研究的目的:
- 开发一种用于高频低通波器建模的新方法.
- 通过整合热和机械因素来提高预测建模的准确性.
- 提高电子元件设计的可靠性和弹性.
主要方法:
- 贝叶斯神经网络 (BNNs) 与拉丁超立方样本 (LHS) 的协同组合.
- 在COMSOL模拟中进行先进的热机械替代模型建模.
- 整合物理尺寸的变化,热效应和机械变形.
主要成果:
- 在EM系统的预测建模忠实性方面表现出显著的飞跃.
- 实现了强大的概括能力,在未见的数据上进行验证.
- 通过将热变量与维度参数集成,展示了改进的模拟精度.
结论:
- 这种新的方法提供了对EM系统中的多物理现象的细微了解.
- 这项研究挑战了传统方法,增强了电子元件设计和优化.
- 为未来的机器学习与计算物理的融合铺平了道路,用于变革性的应用.
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