Modeling, analysis, and optimization under uncertainties: a review
Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …
has been an active area of research due to its evident advantages over deterministic design …
Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation
A Marrel, B Iooss - Reliability Engineering & System Safety, 2024 - Elsevier
In the framework of risk assessment, computer codes are increasingly used to understand,
model and predict physical phenomena. As these codes can be very time-consuming to run …
model and predict physical phenomena. As these codes can be very time-consuming to run …
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 …
A survey of machine learning techniques in structural and multidisciplinary optimization
Abstract Machine Learning (ML) techniques have been used in an extensive range of
applications in the field of structural and multidisciplinary optimization over the last few …
applications in the field of structural and multidisciplinary optimization over the last few …
Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout
The deposition of protective coatings on nuclear fuel cladding has been considered as a
near-term Accident Tolerant Fuel (ATF) concept that will reduce the high-temperature …
near-term Accident Tolerant Fuel (ATF) concept that will reduce the high-temperature …
An open time-series simulated dataset covering various accidents for nuclear power plants
Nuclear energy plays an important role in global energy supply, especially as a key low-
carbon source of power. However, safe operation is very critical in nuclear power plants …
carbon source of power. However, safe operation is very critical in nuclear power plants …
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 …
Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments
In this paper, we developed a machine learning-based Bayesian approach to inversely
quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) …
quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) …
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 …
A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems
In this work, we propose an Inverse Uncertainty Quantification (IUQ) approach to assigning
Probability Density Functions (PDFs) to uncertain input parameters of Thermal-Hydraulic …
Probability Density Functions (PDFs) to uncertain input parameters of Thermal-Hydraulic …