Random Forest and Extra Trees trained on PIC data predict planar multipactor susceptibility maps about as well as a Monte Carlo benchmark, while neural networks generalize more poorly to unseen materials.
# in the (V$%, f×d) plane (Fig. 1), following the similarity scaling law of Vaughan (1988), where f is the excitation frequency. Regions where δ!
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A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry
Random Forest and Extra Trees trained on PIC data predict planar multipactor susceptibility maps about as well as a Monte Carlo benchmark, while neural networks generalize more poorly to unseen materials.