RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction

ArXi:2508.16403v2 Announce Type: replace Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear, layout-sensitive behavior and the high computational cost of traditional simulation tools. Existing machine learning (ML) surrogates often require large datasets to generalize across various topologies or are not accurate on unseen circuits.