| Explainable machine learning for breakdown prediction in high gradient rf cavities O Christoph, CM Thomas, A Andrea, M William, F Lukas, F Lorenz, ... Phys. Rev. Accel. Beams 25 (10), 21, 2022 | 19* | 2022 |
| JACoW: Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators C Obermair, A Apollonio, W Wuensch, L Felsberger, T Cartier-Michaud, ... JACoW IPAC 2021, 1068-1071, 2021 | 4 | 2021 |
| JACoW: Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC C Obermair, A Verweij, A Apollonio, F Pernkopf, M Maciejewski, ... JACoW IPAC 2021, 1072-1075, 2021 | 2 | 2021 |
| Example or prototype? learning concept-based explanations in time-series C Obermair, A Fuchs, F Pernkopf, L Felsberger, A Apollonio, D Wollmann Asian Conference on Machine Learning, 816-831, 2023 | 1 | 2023 |
| Signal monitoring for the LHC-Development of an application for analyzing the main quadrupole busbar resistance C Obermair | 1 | 2018 |
| Interpretable Anomaly Detection in the LHC Main Dipole Circuits with Non-Negative Matrix Factorization C Obermair, A Apollonio, Z Charifoulline, L Felsberger, M Janitschke, ... IEEE Transactions on Applied Superconductivity, 2024 | | 2024 |
| Workshop on Dust Charging and Beam-Dust Interaction in Particle Accelerators MR Blaszkiewicz, X Wang, C Obermair, GJ Rosaz, L Felsberger, ... | | 2023 |
| Anomaly Detection in Conditioning Procedures M Hofmann-Wellenhof, C Obermair | | 2022 |
| Data Augmentation for Breakdown Prediction in CLIC RF Cavities H Bovbjerg, A Apollonio, C Obermair, T Cartier-Michaud, D Wollmann, ... JACoW IPAC 2022, 1553-1556, 2022 | | 2022 |
| Extension of Signal Monitoring Applications with Machine Learning C Obermair Graz, Tech. U., 2020 | | 2020 |
| Interpretable Fault Prediction for CERN Energy Frontier Colliders C Obermair Graz University of Technology (AT), 0 | | |
Franz PernkopfGraz University of TechnologyVerified email at tugraz.at
