amalea

Motivation and Goals of the Project we are seeing an explosion of the amount of data in all accelerator based research fields this is true for all large scale research infrastructures in the research field of Mater: LHC, Belle-2 and future accelerators, EU-XFEL, PETRA-III/IV, BESSY, KARA,.... Machine Learning (ML) promises a possible quantum leap in scientific computing and big data analysis enormous compute power through dedicated hardware (GPUs, FPGAs,...) high performance und flexibility in the analysis of complex data Goals: systematic evaluation and application of ML methods to Particle Physics, Accelerator research and photon science explore possibilities and limits development of a solid hardware and software infrastructure in the centres exploit synergies for establishing cross disciplinary competences in the application of AI/ML methods in the the physics research Read more about amalea »

Publications

1. Physics-based deep neural networks for beam dynamics in charged particle accelerators: Andrei Ivanov et al., Phys. Rev. Accel. Beams, doi: 10.1103/PhysRevAccelBeams.23.074601

Team Members

	Ilya Agapov	Scientific areas: accelerator
	Annika Eichler	Scientific areas: accelerator
	Frank Gaede	Scientific areas: particles

Project Partner