ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2307.05592
  4. Cited By
Functional PCA and Deep Neural Networks-based Bayesian Inverse
  Uncertainty Quantification with Transient Experimental Data

Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data

10 July 2023
Ziyue Xie
M. Yaseen
Xuechun Wu
ArXiv (abs)PDFHTML

Papers citing "Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data"

2 / 2 papers shown
Title
Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
Xu Wu
L. Moloko
P. Bokov
Gregory K. Delipei
Joshua Kaizer
K. Ivanov
AI4CE
71
0
0
16 Mar 2025
An Investigation on Machine Learning Predictive Accuracy Improvement and
  Uncertainty Reduction using VAE-based Data Augmentation
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation
Farah Alsafadi
M. Yaseen
Xu Wu
85
0
0
24 Oct 2024
1