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 …
Data-enabled physics-informed machine learning for reduced-order modeling digital twin: application to nuclear reactor physics
This paper proposes an approach that combines reduced-order models with machine
learning in order to create physics-informed digital twins to predict high-dimensional output …
learning in order to create physics-informed digital twins to predict high-dimensional output …
Bayesian analysis of parametric uncertainties and model form probabilities for two different crystal plasticity models of lamellar grains in α+ β titanium alloys
The properties of individual phases and complex interactions of phases that affect
mechanical behavior of metastable lamellar and Widmanstaetten morphologies of α+ β …
mechanical behavior of metastable lamellar and Widmanstaetten morphologies of α+ β …
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) …
Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning
To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-
scale framework was proposed in this paper by applying a physics-guided data-driven …
scale framework was proposed in this paper by applying a physics-guided data-driven …
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 …
Recent advances in uncertainty quantification in structural response characterization and system identification
Structural dynamics has numerous practical applications, such as structural analysis,
vibration control, energy harvesting, system identification, structural safety assessment, and …
vibration control, energy harvesting, system identification, structural safety assessment, and …
A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation
Over the past decades, several computer codes have been developed for simulation and
analysis of thermal-hydraulics and system response in nuclear reactors under operating …
analysis of thermal-hydraulics and system response in nuclear reactors under operating …
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 …