用贝叶斯在线学习，仅用2.86%仿真时间实现高精度射频无源器件建模。
Efficient RF Passive Components Modeling with Bayesian Online Learning and Uncertainty Aware Sampling
- 基于可重构头的贝叶斯神经网络，联合建模几何与频率域并量化不确定性。
- 自适应采样策略结合不确定性引导，优化跨参数与频率的数据采集。
- 在3个器件上实现35倍加速，仅需传统方法2.86%的仿真时间。
基于机器学习的传统射频（RF）无源器件建模依赖大量电磁（EM）仿真以覆盖几何与频率设计空间，造成计算瓶颈。本文提出一种不确定性感知的贝叶斯在线学习框架，用于高效参数化建模：1）采用具有可重构头的贝叶斯神经网络，联合建模几何与频率域并量化不确定性；2）设计自适应采样策略，利用不确定性指导，在几何参数与频率域间协同优化训练数据采样。在三个射频无源器件上验证，该框架在仅使用传统机器学习方法2.86%的电磁仿真时间下实现高精度建模，达到35倍加速效果。
原文摘要 · Abstract (English)
Conventional radio frequency (RF) passive components modeling based on machine learning requires extensive electromagnetic (EM) simulations to cover geometric and frequency design spaces, creating computational bottlenecks. In this paper, we introduce an uncertainty-aware Bayesian online learning framework for efficient parametric modeling of RF passive components, which includes: 1) a Bayesian neural network with reconfigurable heads for joint geometric-frequency domain modeling while quantifying uncertainty; 2) an adaptive sampling strategy that simultaneously optimizes training data sampling across geometric parameters and frequency domain using uncertainty guidance. Validated on three RF passive components, the framework achieves accurate modeling while using only 2.86% EM simulation time compared to traditional ML-based flow, achieving a 35 times speedup.
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