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 …
Switchtab: Switched autoencoders are effective tabular learners
Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …
computer vision and natural language processing (NLP), where data samples exhibit explicit …
Recontab: Regularized contrastive representation learning for tabular data
Representation learning stands as one of the critical machine learning techniques across
various domains. Through the acquisition of high-quality features, pre-trained embeddings …
various domains. Through the acquisition of high-quality features, pre-trained embeddings …
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 …
Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review
In traditional slope stability analysis, it is assumed that some “average” or appropriately
“conservative” properties operate over the entire region of interest. This kind of deterministic …
“conservative” properties operate over the entire region of interest. This kind of deterministic …
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) …
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 …
Analyzing the effects of various isotropic and anisotropic kernels on critical heat flux prediction using Gaussian process regression
M Soleimani, M Esmaeilbeigi, R Cavoretto… - … Applications of Artificial …, 2024 - Elsevier
The critical heat flux (CHF) is an important parameter determining the heat transfer capability
of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is …
of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is …
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) …
Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data
This work focuses on developing an inverse uncertainty quantification (IUQ) process for time-
dependent responses, using dimensionality reduction by functional principal component …
dependent responses, using dimensionality reduction by functional principal component …