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.
Under the leave-one-material-out cross-validation strategy, the held-out material occupies a region in feature space that is entirely disjoint from the training data
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.acc-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.