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Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89

https://doi.org/10.1002/fld.4504
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Global Sensitivity Analysis. The Primer
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Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Kernel Smoothing
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Sensitivity Analysis of Spatial and/or Temporal Phenomena
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Handbook of Uncertainty Quantification
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Design of Experiments
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Early South African Geostatistical Techniques in Today’s Perspective
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Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows
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Assessment and propagation of model uncertainty
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Towards an adaptive POD/SVD surrogate model for aeronautic design
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High performance parallel computing of flows in complex geometries
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Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models
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A hybrid anchored-ANOVA – POD/Kriging method for uncertainty quantification in unsteady high-fidelity CFD simulations
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Derivative based global sensitivity measures and their link with global sensitivity indices
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Recent advances in surrogate-based optimization
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Calculations of Sobol indices for the Gaussian process metamodel
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Trends in sensitivity analysis practice in the last decade
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Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms
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Subgrid-Scale Stress Modelling Based on the Square of the Velocity Gradient Tensor
inferredcites →https://doi.org/10.1057/jos.2013.16openalex-snapshot
Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties
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Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
inferredcites →https://doi.org/10.1090/qam/910462openalex-snapshot
Turbulence and the dynamics of coherent structures. I. Coherent structures
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Multi-fidelity optimization via surrogate modelling
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An importance quantification technique in uncertainty analysis for computer models
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Geometric separator theorems and applications
inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02015.xopenalex-snapshot
Assessment and Propagation of Model Uncertainty
inferredcites →https://doi.org/10.1115/90-gt-358openalex-snapshot
Aero-Thermal Performance of a Two Dimensional Highly Loaded Transonic Turbine Nozzle Guide Vane: A Test Case for Inviscid and Viscous Flow Computations
inferredcites →https://doi.org/10.1115/gt2010-22793openalex-snapshot
Advanced Numerical Simulation Dedicated to the Prediction of Heat Transfer in a Highly Loaded Turbine Guide Vane
inferredcites →https://doi.org/10.1115/gt2015-43133openalex-snapshot
Direct Numerical Simulations of a High Pressure Turbine Vane
inferredcites →https://doi.org/10.1115/gt2016-56798openalex-snapshot
Uncertainty Quantification in Turbomachinery Simulations
inferredcites →https://doi.org/10.1115/gt2016-57189openalex-snapshot
Highly Resolved LES of a Linear HPT Vane Cascade Using Structured and Unstructured Codes
inferredcites →https://doi.org/10.1115/gt2017-64835openalex-snapshot
Uncertainty Quantification in Large Eddy Simulations of a Rich-Dome Aviation Gas Turbine
inferredcites →https://doi.org/10.1137/15m1046812openalex-snapshot
Comparison of Surrogate-Based Uncertainty Quantification Methods for Computationally Expensive Simulators
inferredcites →https://doi.org/10.1146/annurev.fluid.010908.165248openalex-snapshot
Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics
inferredcites →https://doi.org/10.1198/tech.2010.09157openalex-snapshot
Generalized Latin Hypercube Design for Computer Experiments
inferredcites →https://doi.org/10.1201/9781420034899openalex-snapshot
Design and Modeling for Computer Experiments
inferredcites →https://doi.org/10.1214/ss/1177012413openalex-snapshot
Design and Analysis of Computer Experiments
inferredcites →https://doi.org/10.13122/fontaneto-fabrizio_phd2014-05-22openalex-snapshot
Aero-thermal performance of a film-cooled high pressure turbine blade/vane: a test case for numerical codes validation
inferredcites →https://doi.org/10.21105/joss.00493openalex-snapshot
BATMAN: Statistical analysis for expensive computer codes made easy
inferredcites →https://doi.org/10.2307/1268522openalex-snapshot
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
inferredcites →https://doi.org/10.2514/1.37808openalex-snapshot
Computational-Fluid-Dynamics-Based Kriging Optimization Tool for Aeronautical Combustion Chambers
inferredcites →https://doi.org/10.2514/1.8650openalex-snapshot
Use of Kriging Models to Approximate Deterministic Computer Models
inferredcites →https://doi.org/10.2514/1.c032698openalex-snapshot
Uncertainty Quantification for Airfoil Icing Using Polynomial Chaos Expansions
inferredcites →https://doi.org/10.29008/etc2017-159openalex-snapshot
LES of the LS89 cascade: influence of inflow turbulence on the flow predictions
inferredcites →https://doi.org/10.48550/arxiv.1001.1049openalex-snapshot
Numerical studies of the metamodel fitting and validation processes
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Model selection based on validation criteria for Gaussian process regression: An application with highlights on the predictive variance
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Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
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The Current State of High-Fidelity Simulations for Main Gas Path Turbomachinery Components and Their Industrial Impact
inferred← citeshttps://doi.org/10.1007/s10596-019-09875-yopenalex-snapshot
Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks
inferred← citeshttps://doi.org/10.1016/j.ijheatmasstransfer.2019.119264openalex-snapshot
Design optimization of an heat exchanger using Gaussian process
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Discovering optimal flapping wing kinematics using active deep learning
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Routes towards an effective AI in CFD: an epistemological and technical perspective
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High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multifidelity Deep Neural Networks
inferred← citeshttps://doi.org/10.21105/joss.00493openalex-snapshot
BATMAN: Statistical analysis for expensive computer codes made easy
inferred← citeshttps://doi.org/10.2514/1.j062533openalex-snapshot
Latent Representation of Computational Fluid Dynamics Meshes and Application to Airfoil Aerodynamics
inferred← citeshttps://doi.org/10.48550/arxiv.1912.04706openalex-snapshot
Improved Surrogate Modeling using Machine Learning for Industrial Civil Aircraft Aerodynamics
inferred← citeshttps://doi.org/10.70675/457e93fbz14e0z4054z8881zac2090d335a7openalex-snapshot
Uncertainty quantification in high dimensional problems
inferred← citeshttps://doi.org/10.70675/71187689zde88z4549z81d4z14f2c5bf7cd7openalex-snapshot
Surrogate models coupled with machine learning to approximate complex physical phenomena involving aerodynamic and aerothermal simulations
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Multiphysics coupled simulations of gas turbines
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