Speaker
Minghao Song
(Brookhaven National Laboratory)
Description
The impedance of in-vacuum undulators (IVUs) significantly affect the broadband impedance and, consequently, the beam dynamics in storage rings. During the IVU design phase, numerous iterative discussions between physicists and engineers are required, often involving extensive simulations of the complete 3D geometry, a few meters long, using limited computational resources. In this paper, we propose training a Gaussian process model with limited simulation data to emulate the physical model. We compare the predictions of the trained model to the simulation data and explore its application in optimizing the IVU design.
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Author
Minghao Song
(Brookhaven National Laboratory)
Co-authors
Aamna Khan
(Brookhaven National Laboratory)
Victor Smaluk
(Brookhaven National Laboratory)
Guimei Wang
(Brookhaven National Laboratory)
Michael Seegitz
(National Synchrotron Light Source II)
