Research graph
References from Campaign-disjoint validation of machine-learned critical heat flux models: leakage quantification and physics-anchored residual transfer. Local targets link to admitted publications; unresolved targets remain external evidence.
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1990 · External reference
10.1093/oso/9780198562825.001.0001
10.1093/oso/9780198562825.001.0001 · External reference
Prediction of critical heat flux in flow boiling at low qualities
10.1016/s0017-9310(83)80047-7 · 1983 · External reference
A mechanistic critical heat flux model for subcooled flow boiling based on local bulk flow conditions
10.1016/0301-9322(88)90070-5 · 1988 · External reference
Assessment of correlations and models for the prediction of CHF in water subcooled flow boiling
1994 · External reference
Consideration of critical heat flux margin prediction by subcooled or low quality critical heat flux correlations
10.1016/0029-5493(95)01169-2 · 1996 · External reference
10.1201/9781315138510
10.1201/9781315138510 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
External reference
Studies on burnout; Part 3-a new correlation for round ducts and uniform heating and its comparison with world data
1967 · External reference
An improved version of the generalized correlation of critical heat flux for the forced convective boiling in uniformly heated vertical tubes
10.1016/0017-9310(84)90276-x · 1984 · External reference
Prediction of departure from nucleate boiling for an axially non-uniform heat flux distribution
10.1016/s0022-3107(67)90054-8 · 1967 · External reference
Critical heat flux (CHF) for water flow in tubes—II
2000 · External reference
Critical heat flux for water flow in tubes. II. Subcooled CHF correlations
10.1016/s0017-9310(99)00192-1 · 2000 · External reference
Unresolved reference
External reference
The 2006 CHF look-up table
10.1016/j.nucengdes.2007.02.014 · 2007 · External reference
On the prediction of critical heat flux using a physics-informed machine learning-aided framework
2020 · External reference
Comparison of Standalone and Hybrid Machine Learning Models for Prediction of Critical Heat Flux in Vertical Tubes
10.3390/en16073182 · 2023 · External reference
Uncertainty quantification study of the physics-informed machine learning models for critical heat flux prediction
10.1016/j.pnucene.2024.105097 · 2024 · External reference
Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks
10.1016/j.anucene.2025.111502 · 2025 · External reference
Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification
10.1016/j.applthermaleng.2025.127447 · 2025 · External reference
Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux
2026 · External reference
10.13182/nureth20-40108
10.13182/nureth20-40108 · External reference
Introducing the OECD/NEA WPRS Benchmark on Artificial Intelligence and Machine Learning for Scientific Computing in Nuclear Engineering
2023 · External reference
Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux Over a Large Parameter Space
2024 · External reference
Advancing nuclear energy safety via hybrid stacking models: Integrating physics-informed machine learning and traditional AI for critical heat flux prediction
10.1016/j.pnucene.2026.106326 · 2026 · External reference
Critical heat flux prediction for internally heated annuli with uniform power
10.1016/j.nucengdes.2025.114685 · 2026 · External reference
Unresolved reference
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A survey of cross-validation procedures for model selection
10.1214/09-ss054 · 2010 · External reference
The Elements of Statistical Learning
2009 · External reference
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
10.1111/ecog.02881 · 2017 · External reference
Critical heat flux data used to generate the 2006 Groeneveld critical heat flux lookup tables. United States Nuclear Regulatory Commission
2019 · External reference
10.1007/978-3-662-53219-5
10.1007/978-3-662-53219-5 · External reference
Scikit-learn: Machine Learning in Python
2011 · External reference
Greedy function approximation: A gradient boosting machine
10.1214/aos/1013203451 · 2001 · External reference
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
2017 · External reference
Unresolved reference
2017 · External reference
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
10.1016/j.artint.2021.103502 · 2021 · External reference
10.1007/978-1-4899-4541-9
10.1007/978-1-4899-4541-9 · External reference
Determination of Sample Sizes for Setting Tolerance Limits
10.1214/aoms/1177731788 · 1941 · External reference
10.21236/ad0148179
10.21236/ad0148179 · External reference
10.1561/9781638281597
10.1561/9781638281597 · External reference
