ICALEPCS2019 - List of Authors (Wang, W.)

Paper	Title	Page
TUCPL06	Accelerating Machine Learning for Machine Physics (an AMALEA-project at KIT)	781
	T. Boltz, E. Bründermann, M. Caselle, A. Kopmann, W. Mexner, A.-S. Müller, W. Wang KIT, Karlsruhe, Germany
	The German Helmholtz Innovation Pool project will explore and provide novel cutting edge Machine Learning techniques to address some of the most urgent challenges in the era of large data harvests in accelerator physics. Progress in virtually all areas of accelerator based physics research relies on recording and analyzing enormous amounts of data. This data is produced by progressively sophisticated fast detectors alongside increasingly precise accelerator diagnostic systems. As KIT contribution to AMALEA it is planned to investigate a design of a fast and adaptive feedback system that reacts to small changes in the charge distribution of the electron bunch and establishes extensive control over the longitudinal beam dynamics. As a promising and well-motivated approach, reinforcement learning methods are considered. In a second step the algorithm will be implemented as a pilot experiment to a novel PCIe FPGA readout electronics card based on Zynq UltraScale⁺ MultiProcessor System on-Chip (MPSoC).
	Slides TUCPL06 [5.955 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL06
About •	paper received ※ 27 September 2019 paper accepted ※ 01 November 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Accelerating Machine Learning for Machine Physics (an AMALEA-project at KIT)

781

T. Boltz, E. Bründermann, M. Caselle, A. Kopmann, W. Mexner, A.-S. Müller, W. Wang
KIT, Karlsruhe, Germany

The German Helmholtz Innovation Pool project will explore and provide novel cutting edge Machine Learning techniques to address some of the most urgent challenges in the era of large data harvests in accelerator physics. Progress in virtually all areas of accelerator based physics research relies on recording and analyzing enormous amounts of data. This data is produced by progressively sophisticated fast detectors alongside increasingly precise accelerator diagnostic systems. As KIT contribution to AMALEA it is planned to investigate a design of a fast and adaptive feedback system that reacts to small changes in the charge distribution of the electron bunch and establishes extensive control over the longitudinal beam dynamics. As a promising and well-motivated approach, reinforcement learning methods are considered. In a second step the algorithm will be implemented as a pilot experiment to a novel PCIe FPGA readout electronics card based on Zynq UltraScale⁺ MultiProcessor System on-Chip (MPSoC).

Slides TUCPL06 [5.955 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL06

About •

paper received ※ 27 September 2019 paper accepted ※ 01 November 2019 issue date ※ 30 August 2020

Export •

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