Adolphus Lye:
Probabilistic Prediction of Material Properties with Artificial Intelligence (PROMAP)

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In this talk, Adolphus Lye will discuss his recent work on Project PROMAP which was a feasibility study funded by the Advanced Nuclear Skills and Innovation Campus (ANSIC) as part of the Game Changers Challenge. The aforementioned study looks into addressing the issue of sparse data as well as presenting the opportunities for Artificial Intelligence (AI) within the Nuclear sector which has yet to embrace the latter.
The work presented in this session will include: 1) a brief overview of the state of AI within the Nuclear industry; 2) the method of data-enhancement on a sparse data-set; 3) the introduction of model uncertainty in Artificial Neural Networks; and 4) the amalgamation of Artificial Neural Network with Uncertainty Quantification tools. The result is a general framework to provide a robust probabilistic prediction, with the associated confidence bounds, of Nuclear material properties which is validated with experimental data.
To conclude the session, Adolphus Lye will re-iterate the benefits of the proposed methodology as well as briefly discuss the future research work to be conducted following the conclusion of this feasibility study.

Adolphus Lye is currently a 4th-year PhD student based at the Institute of Risk and Uncertainty within the University of Liverpool. In 2018, he graduated from the National University of Singapore (NUS) with a Bachelor of Science (with Honours) majoring in Physics and minoring in Mathematics. In that same year, he obtained the Singapore Nuclear Research and Safety Initiatives (SNRSI) scholarship which funded his PhD study. He is co-supervised by Professor Edoardo Patelli and Professor Alice Cicirello.
Through the course of his candidature, his research interest mainly revolves around the topic of Bayesian model updating and its applications in addressing Structural and Nuclear Engineering problems. This includes reviewing state-of-the-art developments in Bayesian model updating, addressing model uncertainty in inferring time-varying parameters via On-line Bayesian model updating, and more recently, merging Uncertainty Quantification tools with Artificial Intelligence algorithms to address the issue of sparse data in Nuclear engineering.