Basic framework and main methods of uncertainty quantification
J Zhang, J Yin, R Wang - Mathematical Problems in …, 2020 - Wiley Online Library
Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in
many fields and has been highly valued and strongly supported by academia and industry …
many fields and has been highly valued and strongly supported by academia and industry …
A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes
Uncertainty Quantification (UQ) is an essential step in computational model validation
because assessment of the model accuracy requires a concrete, quantifiable measure of …
because assessment of the model accuracy requires a concrete, quantifiable measure of …
Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis
Polynomial chaos expansion has received considerable attention in uncertainty
quantification since its great modeling capability for complex systems. However, considering …
quantification since its great modeling capability for complex systems. However, considering …
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory
In nuclear reactor system design and safety analysis, the Best Estimate plus Uncertainty
(BEPU) methodology requires that computer model output uncertainties must be quantified …
(BEPU) methodology requires that computer model output uncertainties must be quantified …
Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data
In nuclear reactor fuel performance simulation, fission gas release (FGR) and swelling
involve treatment of several complicated and interrelated physical processes, which …
involve treatment of several complicated and interrelated physical processes, which …
Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation
Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its
reliability is critical for the success of advanced nuclear technologies. However, TRISO …
reliability is critical for the success of advanced nuclear technologies. However, TRISO …
[HTML][HTML] Physics informed neural networks for surrogate modeling of accidental scenarios in nuclear power plants
F Antonello, J Buongiorno, E Zio - Nuclear Engineering and Technology, 2023 - Elsevier
Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling
and Simulation (M&S) to investigate system response to many operational conditions …
and Simulation (M&S) to investigate system response to many operational conditions …
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE
Abstract Inverse Uncertainty Quantification (UQ) is a process to quantify the uncertainties in
random input parameters while achieving consistency between code simulations and …
random input parameters while achieving consistency between code simulations and …
[HTML][HTML] Global sensitivity analysis for segmented inverse uncertainty quantification in the safety analysis of nuclear power plants
Abstract Within the Best Estimate Plus Uncertainty framework for the safety analysis of
Nuclear Power Plants, the quantification of the uncertainties affecting the Thermal …
Nuclear Power Plants, the quantification of the uncertainties affecting the Thermal …
Dynamic mode decomposition for the stability analysis of the Molten Salt Fast Reactor core
The study of innovative nuclear reactors involves the use of increasingly complex numerical
models. While such models provide a high-fidelity description of many non-linear coupled …
models. While such models provide a high-fidelity description of many non-linear coupled …