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Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Model

arXiv 2511.16207 ↗First posted 20 Nov 2025cs.LGcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Deep generative modeling provides a powerful pathway to overcome data scarcity in energy-related applications where experimental data are often limited, costly, or difficult to obtain. By learning the underlying probability distribution of the training dataset, deep generative models, such as the diffusion model (DM), can generate high-fidelity synthetic samples that statistically resemble the training data. Such synthetic data generation can significantly enrich the size and diversity of the available training data, and more importantly, improve the robustness of downstream machine learning models in predictive tasks. The objective of this paper is to investigate the effectiveness of DM for overcoming data scarcity in nuclear energy applications. By leveraging a public dataset on critical heat flux (CHF) that cover a wide range of commercial nuclear reactor operational conditions, we developed a DM that can generate an arbitrary amount of synthetic samples for augmenting of the CHF dataset. Since a vanilla DM can only generate samples randomly, we also developed a conditional DM capable of generating targeted CHF data under user-specified thermal-hydraulic conditions. The performance of the DM was evaluated based on their ability to capture empirical feature distributions and pair-wise correlations, as well as to maintain physical consistency. The results showed that both the DM and conditional DM can successfully generate realistic and physics-consistent CHF data. Furthermore, uncertainty quantification was performed to establish confidence in the generated data. The results demonstrated that the conditional DM is highly effective in augmenting CHF data while maintaining acceptable levels of uncertainty.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 24 of its 47 references that are in the map. First posted on arXiv 20 Nov 2025.

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24 of 47 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Generating Synthetic Net Load Data with Physics-informed Diffusion Model4 Jun
  2. 2024Creating synthetic energy meter data using conditional diffusion and building metadata31 Mar
  3. 2023Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models24 Apr
  4. 2022Diffusion Models Beat GANs on Topology Optimization20 Aug
  5. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation7 Jul

Other references · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2014Conditional Generative Adversarial Netscited by 2,152
  9. 2022Diffusion Models in Vision: A Surveycited by 1,008
  10. 2023Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generationcited by 142
  11. 2021An Introduction to Deep Generative Modelingcited by 104
  12. 2023Diffusion Models Beat GANs on Image Classificationcited by 57
7 more
  1. 2023Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion modelscited by 48
  2. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generationcited by 33
  3. 2024Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networkscited by 6
  4. 2020Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoderscited by 4
  5. 2023Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasetscited by 4
  6. 2022SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networkscited by 2
  7. 2024An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentationcited by 1

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