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 …
Scalable inverse uncertainty quantification by hierarchical bayesian modeling and variational inference
Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of
nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an …
nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an …
SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability
Advanced reactors often involve complicated thermal-fluid (TF) phenomena. Modeling such
phenomena with the traditional one-dimensional (1-D) system code is a challenging task …
phenomena with the traditional one-dimensional (1-D) system code is a challenging task …
Preliminary development of machine learning-based error correction model for low-fidelity reactor physics simulation
MR Oktavian, J Nistor, JT Gruenwald, Y Xu - Annals of Nuclear Energy, 2023 - Elsevier
Better prediction capability in reactor simulation procedures can result in better fuel
planning, increased safety, and compliance with the Technical Specifications. Motivated by …
planning, increased safety, and compliance with the Technical Specifications. Motivated by …
A comprehensive Bayesian framework for the development, validation and uncertainty quantification of thermal-hydraulic models
The development, validation and uncertainty quantification of closure laws used into thermal–
hydraulic system codes is a key issue before applying the BEPU (Best Estimate Plus …
hydraulic system codes is a key issue before applying the BEPU (Best Estimate Plus …
Quantification of deep neural network prediction uncertainties for VVUQ of machine learning models
Recent performance breakthroughs in artificial intelligence (AI) and machine learning (ML),
especially advances in deep learning, the availability of powerful and easy-to-use ML …
especially advances in deep learning, the availability of powerful and easy-to-use ML …
Extension of the CIRCE methodology to improve the Inverse Uncertainty Quantification of several combined thermal-hydraulic models
Abstract The Inverse Uncertainty Quantification (IUQ) of physical correlations used in
thermal-hydraulic system codes is a crucial issue in the BEPU (Best Estimate Plus …
thermal-hydraulic system codes is a crucial issue in the BEPU (Best Estimate Plus …
ARTISANS—Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology
The objective of this Technical Opinion Paper (TOP) is to provide an overview of the
research topics in the ARTISANS (Artificial Intelligence for Simulation of Advanced Nuclear …
research topics in the ARTISANS (Artificial Intelligence for Simulation of Advanced Nuclear …
Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach
Abstract Multiphase Computational Fluid Dynamics (MCFD) based on the two-fluid model is
considered a promising tool to model complex two-phase flow systems. MCFD simulation …
considered a promising tool to model complex two-phase flow systems. MCFD simulation …
Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification …
Accurate predictions and uncertainty quantification (UQ) are essential for decision-making in
risk-sensitive fields such as system safety modeling. Deep ensembles (DEs) are efficient …
risk-sensitive fields such as system safety modeling. Deep ensembles (DEs) are efficient …