:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Furlong, Aidan, Zhao, Xingang, Salko, Robert, Wu, Xu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2502.19357
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Prediction of Critical Heat Flux in Rod Bundles Using Tube-Based Hybrid Machine Learning Models in CTF
by: Furlong, Aidan, et al.
Published: (2026)

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal Hydraulics Code
by: Furlong, Aidan, et al.
Published: (2025)

Development and Deployment of Hybrid ML Models for Critical Heat Flux Prediction in Annulus Geometries
by: Furlong, Aidan, et al.
Published: (2025)

Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian Neural Networks
by: Furlong, Aidan, et al.
Published: (2025)

A Three-Stage Bayesian Transfer Learning Framework to Improve Predictions in Data-Scarce Domains
by: Furlong, Aidan, et al.
Published: (2025)

Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks
by: Alsafadi, Farah, et al.
Published: (2024)

Stroke Prediction using Clinical and Social Features in Machine Learning
by: Chadha, Aidan
Published: (2024)

Uncertainty Quantification in the Tsetlin Machine
by: Helin, Runar, et al.
Published: (2025)

Uncertainty Quantification for Machine Learning in Healthcare: A Survey
by: López, L. Julián Lechuga, et al.
Published: (2025)

Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks
by: Furlong, Aidan, et al.
Published: (2024)

Fair Uncertainty Quantification for Depression Prediction
by: Li, Yonghong, et al.
Published: (2025)

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
by: Ajirak, Marzieh, et al.
Published: (2025)

DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling
by: Akhare, Deepak, et al.
Published: (2023)

Shifting Uncertainty to Critical Moments: Towards Reliable Uncertainty Quantification for VLA Model
by: Tang, Yanchuan, et al.
Published: (2026)

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
by: Nemani, Venkat, et al.
Published: (2023)

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
by: Van Katwyk, Peter, et al.
Published: (2025)

Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantification
by: Pian, Yuzhuang, et al.
Published: (2025)

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
by: Yu, Dahai, et al.
Published: (2025)

Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006
by: Djeddou, Messaoud, et al.
Published: (2024)

The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantification
by: Abeyrathna, K. Darshana, et al.
Published: (2024)

Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification
by: Lafage, Adrien, et al.
Published: (2025)

Predicting BWR Criticality with Data-Driven Machine Learning Model
by: Oktavian, Muhammad Rizki, et al.
Published: (2024)

Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach
by: Gharsallaoui, Mohammed Amine, et al.
Published: (2024)

Towards Uncertainty Quantification in Generative Model Learning
by: Morales, Giorgio, et al.
Published: (2025)

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
by: Bethell, Daniel, et al.
Published: (2023)

Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
by: Neumeier, Marion, et al.
Published: (2024)

Calibrated Physics-Informed Uncertainty Quantification
by: Gopakumar, Vignesh, et al.
Published: (2025)

Quantification of Credal Uncertainty: A Distance-Based Approach
by: Gonzalez-Garcia, Xabier, et al.
Published: (2026)

Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
by: Teufel, Jonas, et al.
Published: (2025)

Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
by: Lützow, Laura, et al.
Published: (2025)

Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency
by: Alqaraleh, Muhyeeddin, et al.
Published: (2025)

Uncertainty Quantification in SVM prediction
by: Anand, Pritam
Published: (2025)

The Role of Ambiguity in Error Prediction via Uncertainty Quantification
by: Staliūnaitė, Ieva Raminta, et al.
Published: (2026)

Uncertainty Quantification for Surface Ozone Emulators using Deep Learning
by: Doerksen, Kelsey, et al.
Published: (2025)

Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
by: He, Wenchong, et al.
Published: (2023)

FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
by: Chen, Hui, et al.
Published: (2024)

Credal Ensemble Distillation for Uncertainty Quantification
by: Wang, Kaizheng, et al.
Published: (2025)

A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers
by: Font, Miguel N., et al.
Published: (2025)

QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
by: Duell, Jamie, et al.
Published: (2024)

Developing Distance-Aware, and Evident Uncertainty Quantification in Dynamic Physics-Constrained Neural Networks for Robust Bearing Degradation Estimation
by: Razzaq, Waleed, et al.
Published: (2025)