Nicole Neveu

TL;DR: In this paper, the authors introduce an approach based on machine learning to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation, which enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.

TL;DR: An approach based on machine learning is introduced to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation that can be reliably used with multi-objective optimization to obtain orders-of-magnitude speedup in initial design studies and experiment planning.

TL;DR: OPAL as mentioned in this paper is a parallel open source tool for charged-particle optics in linear accelerators and rings, including 3D space charge, which can run on a laptop as well as on the largest high performance computing systems.

TL;DR: An overview of the research program at the Argonne Wakefield Accelerator Facility (AWA), including some highlights of recent experiments, can be found in this article, where the authors give an overview of research programs at AWA.

TL;DR: The Two-Beam Acceleration (TBA) is a modified approach to the structure-based wakefield acceleration which may meet the luminosity, efficiency, and cost requirement of a future linear collider as mentioned in this paper.