ICAP2018 - List of Authors (Neveu, N.R.)

Paper	Title	Page
SUPAF12	Surrogate Models for Beam Dynamics in Charged Particle Accelerators
	A.L. Edelen SLAC, Menlo Park, California, USA D. Acharya, A. Adelmann, M. Frey PSI, Villigen PSI, Switzerland N.R. Neveu ANL, Argonne, Illinois, USA
	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]
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SAPAF03	Comparison of Model-Based and Heuristic Optimization Algorithms Applied to Photoinjectors Using Libensemble	22
	N.R. Neveu IIT, Chicago, Illinois, USA S. T. P. Hudson, J.M. Larson ANL, Argonne, Illinois, USA L.K. Spentzouris Illinois Institute of Technology, Chicago, Illinois, USA
	Funding: U.S. DOE, OS, contract DE-AC02-06CH11357 and grant DE-SC0015479. Genetic algorithms are common and often used in the accelerator community. They require large amounts of computational resources and empirical adjustment of hyperparameters. Model based methods are significantly more efficient, but often labeled as unreliable for the nonlinear or unsmooth problems that can be found in accelerator physics. We investigate the behavior of both approaches using a photoinjector operated in the space charge dominated regime. All optimization runs are coordinated and managed by the Python library libEnsemble, which is developed at Argonne National Laboratory.
	Slides SAPAF03 [0.653 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-SAPAF03
About •	paper received ※ 11 November 2018 paper accepted ※ 19 November 2018 issue date ※ 26 January 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

SUPAF12

Surrogate Models for Beam Dynamics in Charged Particle Accelerators

A.L. Edelen
SLAC, Menlo Park, California, USA
D. Acharya, A. Adelmann, M. Frey
PSI, Villigen PSI, Switzerland
N.R. Neveu
ANL, Argonne, Illinois, USA

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]

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reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

SAPAF03

Comparison of Model-Based and Heuristic Optimization Algorithms Applied to Photoinjectors Using Libensemble

22

N.R. Neveu
IIT, Chicago, Illinois, USA
S. T. P. Hudson, J.M. Larson
ANL, Argonne, Illinois, USA
L.K. Spentzouris
Illinois Institute of Technology, Chicago, Illinois, USA

Funding: U.S. DOE, OS, contract DE-AC02-06CH11357 and grant DE-SC0015479.
Genetic algorithms are common and often used in the accelerator community. They require large amounts of computational resources and empirical adjustment of hyperparameters. Model based methods are significantly more efficient, but often labeled as unreliable for the nonlinear or unsmooth problems that can be found in accelerator physics. We investigate the behavior of both approaches using a photoinjector operated in the space charge dominated regime. All optimization runs are coordinated and managed by the Python library libEnsemble, which is developed at Argonne National Laboratory.

Slides SAPAF03 [0.653 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICAP2018-SAPAF03

About •

paper received ※ 11 November 2018 paper accepted ※ 19 November 2018 issue date ※ 26 January 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)