# An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation

arXiv: https://arxiv.org/abs/2410.19063 · HTML: https://mlsanity.com/paper/2410.19063/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://mlsanity.com/paper/2009.14193/) (2020)
- [Bayesian Neural Networks: An Introduction and Survey](https://mlsanity.com/paper/2006.12024/) (2020)
- [An Uncertain Future: Forecasting from Static Images using Variational Autoencoders](https://mlsanity.com/paper/1606.07873/) (2016)
- [Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder](https://mlsanity.com/paper/2004.12811/) (2020)
- [Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models](https://mlsanity.com/paper/2206.14615/) (2022)
- [Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks](https://mlsanity.com/paper/2211.08654/) (2022)
- [Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks](https://mlsanity.com/paper/2409.05790/) (2024)
- [Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets](https://mlsanity.com/paper/2308.10120/) (2023)
- [Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data](https://mlsanity.com/paper/2307.05592/) (2023)
- [Conformal Prediction in Dynamic Biological Systems](https://mlsanity.com/paper/2409.02644/) (2024)

## Built on by (most cited first, 1 of 1)

- [Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Model](https://mlsanity.com/paper/2511.16207/) (2025)

## Abstract (arXiv)

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. We found that augmenting the training dataset using VAEs has improved the DNN model's predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.