Dr. Xu Wu
Assistant Professor
Department of Nuclear Engineering
North Carolina State University

Abstract

Modern predictive simulations have a special focus on the systematic treatment of input, model and data uncertainties and their propagation through a computational model to produce predictions of Quantities-of-Interest (QoIs) with quantified uncertainty. Although the modeling of nuclear reactors has made tremendous progress, there are always discrepancies between ideal in silico designed systems and real-world manufactured ones. As a consequence, uncertainties must be quantified along with simulation to facilitate optimal design and decision making, ensure robustness, performance and safety margins. This seminar will provide an overview of the fundamental concepts in Uncertainty Quantification (UQ) , as well as forward UQ approaches such as sampling-based methods, stochastic spectral methods, surrogate-based methods, etc. Topics on quantifying prediction uncertainties in Machine Learning models will also be briefly covered.

Biography

Dr. Xu Wu was a Postdoctoral Research Associate at the Department of Nuclear Science and Engineering at MIT. He obtained his Bachelor’s degree in nuclear engineering from Shanghai Jiao Tong University in China in 2011. He obtained his Ph.D. degree in Nuclear Engineering in 2017 from the University of Illinois at Urbana-Champaign.

Dr. Wu’s research interests include: Uncertainty and Sensitivity Analysis, Reduced Order Modeling, model selection and validation, stochastic modeling and inference, Machine Learning, etc. He was the winner of the best paper of the ANS “Young Professional Thermal Hydraulics Research Competition” in 2017. He was selected as the “Russell Heath Distinguished Postdoc” by the Idaho National Laboratory in 2018.

Thursday, August 27. 2020
4:00 pm seminar
https://ncsu.zoom.us/j/95465187820
216 Mann Hall

The seminar will be recorded
Due to the current circumstances, refreshments will not be served.