neurips workshop

X. Pang1, S. Thulasidasan2, L. Rybarcyk2 1Apple, 2Los Alamos National Laboratory Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing Systems 2020 Abstract We describe an approach to learning optimal control policies for a large, linear particle accelerator using deep reinforcement learning coupled with a high-fidelity physics engine. The framework consists of an AI controller that uses deep neural networks for state and action-space representation and learns optimal policies using reward signals that are provided by the physics simulator....

A. L. Edelen1, S. G. Biedron2, J. P. Edelen3, S. V. Milton4, P. J. M. van der Slot5 1Colorado State University, 2Element Aero, 3Fermi National Accelerator Laboratory, 4Los Alamos National Laboratory, 5University of Twente Workshop on Deep Learning for Physical Sciences at the Conference on Neural Information Processing Systems 2017 Abstract Free Electron Laser (FEL) facilities often must accommodate requests for a varietyof electron beam parameters in order to supply scientific users with appropriatephoton beam characteristics....