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. This usually requires skilled human operators to tunethe machine. In principle, a neural network control policy that is trained on a broadrange of machine operating states could be used to quickly switch between theserequests without substantial need for human intervention. We present preliminaryresults from an ongoing simulation study in which a neural network control policyis investigated for rapid switching between beam parameters in a compact THzFEL that exhibits nonlinear electron beam dynamics. To accomplish this, we firsttrain a feed-forward neural network to mimic a physics-based simulation of theFEL. We then train a neural network control policy by first pre-training it as aninverse model (using supervised learning with a subset of the simulation data) andthen training it more extensively with reinforcement learning. In this case, thereinforcement learning component consists of letting the policy network interactwith the learned system model and backpropagating the cost through the modelnetwork to the controller network.
Read the paper: https://ml4physicalsciences.github.io/2017/files/nips_dlps_2017_16.pdf
Contact: Auralee Edelen