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TY - UNPB AU - Edelen, A.L. AU - Acharya, D. AU - Adelmann, A. AU - Frey, M. AU - Neveu, N.R. ED - Schaa, Volker RW ED - Makino, Kyoko ED - Snopok, Pavel ED - Berz, Martin TI - Surrogate Models for Beam Dynamics in Charged Particle Accelerators J2 - Proc. of ICAP2018, Key West, FL, USA, 20-24 October 2018 CY - Key West, FL, USA T2 - International Computational Accelerator Physics Conference T3 - 13 LA - english AB - 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 PB - JACoW Publishing CP - Geneva, Switzerland ER -