activity
20152022
most citedExplainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities

19 citations · 33 across the 4 of their papers we have counts for

collaborators

5 papers

physics.acc-ph2022★ 19 cited

Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities

Christoph Obermair, Thomas Cartier-Michaud, Andrea Apollonio +10

The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in par…

physics.comp-ph2016

Optilization of the gyroaverage operator based on hermite interpolation

F Rozar, C Steiner, G Latu +5

Gyrokinetic modeling is appropriate for describing Tokamak plasma turbulence, and the gyroaverage operator is a cornerstone of this approach. In a gyrokinetic code, the gyroaveragi…

physics.plasm-ph2015★ 14 cited

PoPe (Projection on Proper elements) for code control: verification, numerical convergence and reduced models. Application to plasma turbulence simulations

T Cartier-Michaud, P Ghendrih, Y Sarazin +9

The Projection on Proper elements (PoPe) is a novel method of code control dedicated to 1) checking the correct implementation of models, 2) determining the convergence of numerica…

physics.plasm-ph2015

Transport Barrier generation at the interface of regions with different zonal flows dynamics

C. Norscini, Ph. Ghendrih, T. Cartier-Michaud +5

A novel and generic understanding of spontaneous generation of transport barriers and zonation regimes in turbulent self-organization is presented. It associates the barrier onset…

physics.comp-ph2015

Evaluating kernels on Xeon Phi to accelerate Gysela application

G. Latu, M. Haefele, J. Bigot +3

This work describes the challenges presented by porting parts ofthe Gysela code to the Intel Xeon Phi coprocessor, as well as techniques used for optimization, vectorization and tu…