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This archive contains data and supporting material for submission WEPA101 to IPAC23 proceedings.
Subdirectory 03_warpx_training/ contains material for the first example of the article, a laser-plasma accelerator stage. Within 03_warpx_training/ are the two simulation setups, 17_1pC_2stage/ and 19_1_pC_2_beam_center_beam2_pz/. The first contains the necessary material to compute the WarpX simulation we desire to model. The second contains the necessary material to generate the training data using WarpX, as well as Jupyter notebooks for creating and training the neural networks
Subdirectory iota_channel/ contains material for generating ImpactX simulations of the focusing channel based on the IOTA nonlinear lattice as well as creating and training neural networks to learn the dynamics of the focusing channel based on the IOTA lattice
Subdirectory machine_learning_utilities/ contains helper functions for generating, training, and analyzing neural networks
See the readme.md for more information
Notes
This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research
was supported by the Exascale Computing Project (17-SC-20-SC), a
joint project of the U.S. Department of Energy's Office of Science and
National Nuclear Security Administration, responsible for delivering a
capable exascale ecosystem, including software, applications, and hard-
ware technology, to support the nation's exascale computing imperative.
This research used resources of the National Energy Research Scientific
Computing Center, a DOE Office of Science User Facility supported by
the Office of Science of the U.S. Department of Energy under Contract
No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719