@inproceedings{a86f41146c6e445ba22aa5270f4e2c7c,
title = "Design of a deep learning surrogate model for the prediction of FHR design parameters",
abstract = "Following previous work by Xing and Shwageraus, a large corpus of data has been collected for simulated AGR-style fuel assembly design in FHRs. The results exhibit a nonlinear system response, so a {\textquoteleft}deep{\textquoteright} multi-layer perceptron surrogate model is designed and tested for prediction of design parameters. This neuro-surrogate regression model could be useful for the fast optimization of the design parameters, for example in multiobjective optimization problems, due to the extremely fast evaluation time. Source code is made available for the audit and authentication of the scientific method.",
keywords = "AGR, Deep learning, FHR, Fuel design, Neural network, Surrogate model",
author = "Whyte, {A. J.} and Z. Xing and Parks, {G. T.} and E. Shwageraus",
note = "Publisher Copyright: {\textcopyright} 2019 American Nuclear Society. All rights reserved.; 2019 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019 ; Conference date: 25-08-2019 Through 29-08-2019",
year = "2019",
month = jan,
day = "1",
language = "English",
series = "International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019",
publisher = "American Nuclear Society",
pages = "298--307",
booktitle = "International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019",
address = "United States",
}