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 …
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-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results
Boiling heat transfer occurs in many situations and can be used for thermal management in
various engineered systems with high energy density, from power electronics to heat …
various engineered systems with high energy density, from power electronics to heat …
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 …
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 …
Leveraging Industry 4.0: Deep Learning, Surrogate Model, and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System
Industry 4.0 targets the conversion of the traditional industries into intelligent ones through
technological revolution. This revolution is only possible through innovation, optimization …
technological revolution. This revolution is only possible through innovation, optimization …
Inverse uncertainty quantification by hierarchical bayesian modeling and application in nuclear system thermal-hydraulics codes
Inverse Uncertainty Quantification (IUQ) method has been widely used to quantify the
uncertainty of Physical Model Parameters (PMPs) in nuclear Thermal Hydraulics (TH) …
uncertainty of Physical Model Parameters (PMPs) in nuclear Thermal Hydraulics (TH) …
Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments
The two-fluid model-based Multiphase Computational Fluid Dynamics (MCFD) solvers are
promising tools for a variety of engineering problems related to multiphase flows. Such a …
promising tools for a variety of engineering problems related to multiphase flows. Such a …
Integrated framework for model assessment and advanced uncertainty quantification of nuclear computer codes under bayesian statistics
A framework for model evaluation and uncertainty quantification (UQ) is presented with
applications oriented to nuclear engineering simulation codes. Our framework is inspired by …
applications oriented to nuclear engineering simulation codes. Our framework is inspired by …