J. Nucl. Eng., Volume 1, Issue 1 (December 2020) – 6 articles
Cover Story (view full-size image): The possibility is investigated of using artificial neural networks (ANNs) as surrogate models for uncertainty quantification and data assimilation in fuel performance studies. The focus is on steady-state cases with given densification, creep, fission gas, etc. Results show that one can make use of simple rectangular ANNs with one or two hidden layers, with improved results when a non-linear activation function is employed in at least one of the layers. By using 1000 labeled data and a two-layer ANN, one can reduce the computational cost of DA studies by several tens of times while negligibly affecting the result of the DA process. View this paper
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