The purpose of this NOTICE file is to provide legal notices and acknowledgments that must be displayed to users in any derivative works or distributions. This file does not alter the terms of the Apache 2.0 license that governs the use and distribution of the TGLFNN.jl package.
TGLFNN.jl was originally developed by the GA Theory division of the Magnetic Fusion Energy group at General Atomics. This work is supported by the U.S. DOE under DE-SC0018990 (MAST-U), DE-SC0024426 (FDP), DE-SC0022031 (ALF), DE-SC0017992 (AToM), DE-FG02-95ER54309 (GA Theory), DE-FC02-04ER54698 (DIII-D), and used computational resources at NERSC (DOE-AC02-05CH11231). Part of this work has been funded by the United Kingdom EPSRC Energy Programme EP/W006839/1 and the DOE Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships (SULI) program.
If this software contributes to an academic publication, please cite it as follows:
TGLF-NN:
Tom Neiser, Orso Meneghini, Sterling Smith, Joseph McClenaghan, David Orozco, Joseph Hall, Gary Staebler, Emily Belli, Jeff Candy, Database generation for validation of TGLF and retraining of neural network accelerated TGLF-NN, APS Division of Plasma Physics Meeting Abstracts (2022)
GKNN:
Tom Neiser, Orso Meneghini, Sterling Smith, Joseph McClenaghan, Tim Slendebroek, David Orozco, Brian Sammuli, Gary Staebler, Joseph Hall, Emily Belli, Jeff Candy, Multi-fidelity neural network representation of gyrokinetic turbulence, APS Division of Plasma Physics Meeting Abstracts (2023)
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Copyright (c) 2024 General Atomics
Version: v1.0