A A Pilot Review of State-of-the-Art Deep Learning Applications to Nuclear Engineering & Technology
DOI:
https://doi.org/10.14738/aivp.94.10798Keywords:
Deep Learning, Nuclear Energy, Review, Fault Detection and Diagnostics, Monitoring, Proliferation and ResistanceAbstract
Given the advent of big data (BD), the focus of this “pilot” review of the most recent literature is upon deep learning (DL) implementations/solutions to important, as well as inherently complex, nuclear engineering and technology (NET) problems. The potential advancement from implementing large/real-scale DL-based algorithms is critically detailed, as well as highlighted, by inspecting the relevant body of latest research which appears in two major – overlapping, yet also complementary to each other in terms of scope and audience - nuclear energy (NE)-dedicated scientific research means of publication, namely Elsevier’s “Annals of Nuclear Energy” and “Progress in Nuclear Energy” journals. It is demonstrated that, given their (I) accuracy, robustness-to-noise and resilience to incomplete/erroneous information, as well as (II) competence under non-stationary and transient nuclear reactor (NR) operating conditions, deep and convolutional neural networks (DNNs and CNNs, respectively), as well as their variants, are becoming increasingly important, in their own right, for the swift and reliable solution of NET problems. It will be of great interest for – while also advantageous to - the NE, BD, and computational intelligence (CI)/DL communities, to join forces, follow up, and contribute to future research in – as well as application to - this particularly interesting and fruitful collaboration between disciplines.
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