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 …
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 …
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 …
Deep representation learning for multi-functional degradation modeling of community-dwelling aging population
As the aging population grows, particularly for the baby boomer generation, the United
States is witnessing a significant increase in the elderly population experiencing …
States is witnessing a significant increase in the elderly population experiencing …
Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data
The best estimate plus uncertainty methodology in nuclear system thermal-hydraulic studies
necessitates a comprehensive understanding of uncertainties in system code predictions …
necessitates a comprehensive understanding of uncertainties in system code predictions …
Hierarchical Bayesian Modeling for Time-Dependent Inverse Uncertainty Quantification
C Wang - arXiv preprint arXiv:2401.00641, 2024 - arxiv.org
This paper introduces a novel hierarchical Bayesian model specifically designed to address
challenges in Inverse Uncertainty Quantification (IUQ) for time-dependent problems in …
challenges in Inverse Uncertainty Quantification (IUQ) for time-dependent problems in …
Application of the Modular Bayesian Approach for Inverse Uncertainty Quantification in Nuclear Thermal-Hydraulics Systems
C Wang - arXiv preprint arXiv:2404.04774, 2024 - arxiv.org
In the framework of BEPU (Best Estimate plus Uncertainty) methodology, the uncertainties
involved in the simulations must be quantified to prove that the investigated design is …
involved in the simulations must be quantified to prove that the investigated design is …