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

• URL: http://arxiv.org/abs/2511.16207v1
• Date: Thu, 20 Nov 2025 10:19:03 GMT
• Title: Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Model
• Authors: Farah Alsafadi, Alexandra Akins, Xu Wu,
• Abstract summary: Deep generative models, such as the diffusion model (DM), can generate high-fidelity synthetic samples that statistically resemble the training data.<n>This paper investigates the effectiveness of DM for overcoming data scarcity in nuclear energy applications.
• Score: 44.6164235303852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference [89.5628648718851]
  Causal inference is essential for developing and evaluating medical interventions.<n>Real-world medical datasets are often difficult to access due to regulatory barriers.<n>We present STEAM: a novel method for generating Synthetic data for Treatment Effect Analysis in Medicine.
  arXiv  Detail & Related papers  (2025-10-21T16:16:00Z)
• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• Sparse-to-Sparse Training of Diffusion Models [13.443846454835867]
  This paper introduces, for the first time, the paradigm of sparse-to-sparse training to DMs.<n>We focus on unconditional generation and train sparse DMs from scratch on six datasets.<n>Our experiments show that sparse DMs are able to match and often outperform their counterparts, while substantially reducing the number of trainable parameters and FLOPs.
  arXiv  Detail & Related papers  (2025-04-30T07:28:11Z)
• TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation [26.116599951658454]
  Time-series generation is crucial for advancing clinical machine learning models.<n>We argue that fidelity to observed data alone does not guarantee better model performance.<n>We propose TarDiff, a novel target-oriented diffusion framework that integrates task-specific influence guidance.
  arXiv  Detail & Related papers  (2025-04-24T14:36:10Z)
• SIDE: Surrogate Conditional Data Extraction from Diffusion Models [32.18993348942877]
  We present textbfSurrogate condItional Data Extraction (SIDE), a framework that constructs data-driven surrogate conditions to enable targeted extraction from any DPM.<n>We show that SIDE can successfully extract training data from so-called safe unconditional models, outperforming baseline attacks even on conditional models.<n>Our work redefines the threat landscape for DPMs, establishing precise conditioning as a fundamental vulnerability and setting a new, stronger benchmark for model privacy evaluation.
  arXiv  Detail & Related papers  (2024-10-03T13:17:06Z)
• Extracting Training Data from Unconditional Diffusion Models [76.85077961718875]
  diffusion probabilistic models (DPMs) are being employed as mainstream models for generative artificial intelligence (AI) We aim to establish a theoretical understanding of memorization in DPMs with 1) a memorization metric for theoretical analysis, 2) an analysis of conditional memorization with informative and random labels, and 3) two better evaluation metrics for measuring memorization. Based on the theoretical analysis, we propose a novel data extraction method called textbfSurrogate condItional Data Extraction (SIDE) that leverages a trained on generated data as a surrogate condition to extract training data directly from unconditional diffusion models.
  arXiv  Detail & Related papers  (2024-06-18T16:20:12Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets [3.341975883864341]
  This paper explores the applications of deep generative models (DGMs) that have been widely used for image data generation to scientific data augmentation. Once trained, DGMs can be used to generate synthetic data that are similar to the training data and significantly expand the dataset size.
  arXiv  Detail & Related papers  (2023-08-19T22:19:41Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Embedded-physics machine learning for coarse-graining and collective variable discovery without data [3.222802562733787]
  We present a novel learning framework that consistently embeds underlying physics. We propose a novel objective based on reverse Kullback-Leibler divergence that fully incorporates the available physics in the form of the atomistic force field. We demonstrate the algorithmic advances in terms of predictive ability and the physical meaning of the revealed CVs for a bimodal potential energy function and the alanine dipeptide.
  arXiv  Detail & Related papers  (2020-02-24T10:28:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.