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@InProceedings{boltz:icalepcs2019-tucpl06,
author = {T. Boltz and E. Bründermann and M. Caselle and A. Kopmann and W. Mexner and A.-S. Müller and W. Wang},
% author = {T. Boltz and E. Bründermann and M. Caselle and A. Kopmann and W. Mexner and A.-S. Müller and others},
% author = {T. Boltz and others},
title = {{Accelerating Machine Learning for Machine Physics (an AMALEA-project at KIT)}},
booktitle = {Proc. ICALEPCS'19},
pages = {781--788},
paper = {TUCPL06},
language = {english},
keywords = {controls, bunching, hardware, FPGA, storage-ring},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-TUCPL06},
url = {https://jacow.org/icalepcs2019/papers/tucpl06.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL06},
abstract = {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).},
}