SUPAF12
SLAC, Menlo Park, California, USA
PSI, Villigen PSI, Switzerland
ANL, Argonne, Illinois, USA
| Paper | Title | Page |
|---|---|---|
SUPAF12 |
Surrogate Models for Beam Dynamics in Charged Particle Accelerators | |
|
||
| High-fidelity, PIC-based beam dynamics simulations are time and resource intensive. Consider a high dimensional search space, that is far too large to probe with such a high resolution simulation model. We demonstrate that a coarse sampling of the search space can produce surrogate models, which are accurate and fast to evaluate. In constructing the surrogate models, we use artificial neural networks [1] and multivariate polynomial chaos expansion [2]. The performance of both methods are demonstrated in a comparison with high-fidelity simulations, using OPAL, of the Argonne Wakefield Accelerator [3]. We claim that such surrogate models are good candidates for accurate on-line modeling of large, complex accelerator systems. We also address how to estimate the accuracy of the surrogate model and how to refine the surrogate model under changing machine conditions. [1] A. L. Edelen et al., arXiv:1610.06151[physics.acc- ph] [2] A. Adelmann, arXiv:1509.08130v6[physics.acc- ph] [3] N. Neveu et al., 2017 J. Phys.: Conf. Ser. 874 012062 | ||
|
|
Slides SUPAF12 [11.505 MB] | |
| Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |