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RIS citation export for WEPOMS013: Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-Based Charged Particle Beams

TY  - CONF
AU  - Edelen, A.L.
AU  - Emma, C.
AU  - Mayes, C.E.
AU  - Roussel, R.J.
ED  - Zimmermann, Frank
ED  - Tanaka, Hitoshi
ED  - Sudmuang, Porntip
ED  - Klysubun, Prapong
ED  - Sunwong, Prapaiwan
ED  - Chanwattana, Thakonwat
ED  - Petit-Jean-Genaz, Christine
ED  - Schaa, Volker R.W.
TI  - Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-Based Charged Particle Beams
J2  - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022
CY  - Bangkok, Thailand
T2  - International Particle Accelerator Conference
T3  - 13
LA  - english
AB  - Particle accelerators support a wide array of scientific, industrial, and medical applications. To meet the needs of these applications, accelerator physicists rely heavily on detailed simulations of the complicated particle beam dynamics through the accelerator. One of the most computationally expensive and difficult-to-model effects is the impact of Coherent Synchrotron Radiation (CSR). CSR is one of the major drivers of growth in the beam emittance, which is a key metric of beam quality that is critical in many applications. The CSR wakefield is very computationally intensive to compute with traditional electromagnetic solvers, and this is a major limitation in accurately simulating accelerators. Here, we demonstrate a new approach for the CSR wakefield computation using a neural network solver structured in a way that is readily generalizable to new setups. We validate its performance by adding it to a standard beam tracking test problem and show a ten-fold speedup along with high accuracy.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 2261
EP  - 2264
KW  - simulation
KW  - radiation
KW  - wakefield
KW  - synchrotron
KW  - synchrotron-radiation
DA  - 2022/07
PY  - 2022
SN  - 2673-5490
SN  - 978-3-95450-227-1
DO  - doi:10.18429/JACoW-IPAC2022-WEPOMS013
UR  - https://jacow.org/ipac2022/papers/wepoms013.pdf
ER  -