Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data | Paper Citation Record

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This paper cites Multi-level CFD-based Airfoil Shape Optimization With Automated Low- fidelity Model Selection,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-level CFD-based Airfoil Shape Optimization With Automated Low- fidelity Model Selection,

Reference 1

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This paper cites MULTI-FIDELITY MACHINE LEARNING FOR UNCERTAINTY QUANTIFICATION AND OPTIMIZATION,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data MULTI-FIDELITY MACHINE LEARNING FOR UNCERTAINTY QUANTIFICATION AND OPTIMIZATION,

Reference 2

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This paper cites Data-driven model reduction for the Bayesian solution of inverse problems,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Data-driven model reduction for the Bayesian solution of inverse problems,

Reference 3

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Observation 624a84dd-78de-40b7-b073-a481f56cf7d4 · outbound

This paper cites Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference,

Reference 4

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This paper cites Recent Advances in Surrogate Modeling Methods for Uncer- tainty Quantification and Propagation,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Recent Advances in Surrogate Modeling Methods for Uncer- tainty Quantification and Propagation,

Reference 5

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Observation a4091b71-363e-42b6-b9fb-c9e4f2549d48 · outbound

This paper cites Bayesian Calibration of Computer Models,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Bayesian Calibration of Computer Models,

Reference 6

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This paper cites Design and Analysis of Computer Experiments,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Design and Analysis of Computer Experiments,

Reference 7

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This paper cites Experimental and Numerical Study of the Laminar Burning Velocity and Pollutant Emissions of the Mixture Gas of Methane and Carbon Dioxide,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Experimental and Numerical Study of the Laminar Burning Velocity and Pollutant Emissions of the Mixture Gas of Methane and Carbon Dioxide,

Reference 8

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Observation d4a835d8-8684-4f98-9746-894f412fee6e · outbound

This paper cites An overview of statistical learning theory,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data An overview of statistical learning theory,

Reference 9

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Observation f1529e8b-2533-48f8-a88e-c86a43e3ae2a · outbound

This paper cites Hastie, R.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Hastie, R

Reference 10

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Unresolved cited work

Reference 11

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Observation e6598b6b-6ee3-4e9f-9734-31b506ca7ec8 · outbound

This paper cites Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,

Reference 12

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Observation 9abd188d-25d9-4535-a705-71fd5bb78548 · outbound

This paper cites Multifidelity linear regression for scientific machine learning from scarce data,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multifidelity linear regression for scientific machine learning from scarce data,

Reference 13

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Observation 0bb30e1e-15e2-42e3-9614-24962e373b90 · outbound

This paper cites Review of multi-fidelity models.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Review of multi-fidelity models

Reference 14

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This paper cites Overview of Gaussian process based multi-fidelity tech- niques with variable relationship between fidelities, application to aerospace systems,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Overview of Gaussian process based multi-fidelity tech- niques with variable relationship between fidelities, application to aerospace systems,

Reference 15

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Observation c90f39ab-bea5-4f48-a754-b3a97f4b12a4 · outbound

This paper cites MFNets: MULTI-FIDELITY DATA- DRIVEN NETWORKS FOR BAYESIAN LEARNING AND PREDICTION,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data MFNets: MULTI-FIDELITY DATA- DRIVEN NETWORKS FOR BAYESIAN LEARNING AND PREDICTION,

Reference 16

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Observation fa1eedae-0dff-480f-ae6a-cba6816f76a4 · outbound

This paper cites Multifidelity deep operator networks for data-driven and physics-informed problems,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multifidelity deep operator networks for data-driven and physics-informed problems,

Reference 17

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Observation 3c9e07c7-6ac3-4bf5-b5e4-95993912bed6 · outbound

This paper cites A multifidelity approach to continual learning for physical systems,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A multifidelity approach to continual learning for physical systems,

Reference 18

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This paper cites Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport,

Reference 19

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Observation b056950c-5ee3-445a-b9c3-f1cf9f02a7f8 · outbound

This paper cites Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems

Reference 20

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Observation f2a9a3dc-d488-4551-ae13-666c27db20ff · outbound

This paper cites Multilevel Monte Carlo methods,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multilevel Monte Carlo methods,

Reference 21

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Observation fe6cb55a-b24c-4b7b-8998-bb22e4344afe · outbound

This paper cites A generalized approximate con- trol variate framework for multifidelity uncertainty quantification,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A generalized approximate con- trol variate framework for multifidelity uncertainty quantification,

Reference 22

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Observation f1b6cda5-4227-43ea-a724-ca7558512797 · outbound

This paper cites Optimal Model Management for Multifidelity Monte Carlo Estimation,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Optimal Model Management for Multifidelity Monte Carlo Estimation,

Reference 23

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Observation 60496721-b4cb-4e4c-a170-eafca4dc2504 · outbound

This paper cites Grouped approximate control variate estimators.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Grouped approximate control variate estimators

Reference 24

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Observation 4eb6419c-cc38-4f6b-8ee2-7627ab96bcde · outbound

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data On Multilevel Best Linear Unbiased Estimators,

Reference 25

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Observation cd17373e-733e-4529-9b1f-78aec3924e46 · outbound

This paper cites Multifidelity Monte Carlo Es- timation of Variance and Sensitivity Indices,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multifidelity Monte Carlo Es- timation of Variance and Sensitivity Indices,

Reference 26

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Observation 25ba2869-3bc1-4da7-997a-50298b6bb221 · outbound

This paper cites Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders

Reference 27

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Observation 7a295596-791a-46f3-a99c-51ef7052ba59 · outbound

This paper cites A transport-based multifidelity preconditioner for Markov chain Monte Carlo.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A transport-based multifidelity preconditioner for Markov chain Monte Carlo

Reference 28

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Observation c30d60e8-de7c-4a53-9a97-44a0240672d0 · outbound

This paper cites Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo,

Reference 29

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This paper cites Markov chain Monte Carlo Using an Approximation,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Markov chain Monte Carlo Using an Approximation,

Reference 30

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-fidelity Monte Carlo: A pseudo-marginal approach,

Reference 31

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Observation 4442a453-3f4a-4604-96c3-16c6b0429569 · outbound

This paper cites Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes,

Reference 32

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A relaxed localized trust-region reduced basis approach for optimization of multiscale problems,

Reference 33

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This paper cites Available:https://www.esaim-m2an.org/articles/m2an/abs/2024/01/m2an220169/ m2an220169.html.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Available:https://www.esaim-m2an.org/articles/m2an/abs/2024/01/m2an220169/ m2an220169.html

Reference 34

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A Certified Trust Region Reduced Basis Approach to PDE-Constrained Optimization,

Reference 35

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Observation 49e257cc-064a-4fd4-9be2-cf9da5955b1d · outbound

This paper cites Multi-fidelity Learning of Reduced Order Models for Parabolic PDE Constrained Optimization.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-fidelity Learning of Reduced Order Models for Parabolic PDE Constrained Optimization

Reference 36

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This paper cites Multi-Fidelity Methods for Optimization: A Survey.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-Fidelity Methods for Optimization: A Survey

Reference 37

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Observation d417d9b6-3702-48b1-9843-2fcc89844615 · outbound

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes,

Reference 38

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Observation daf52e93-0883-4fbf-a7e7-3bad58e9da1d · outbound

This paper cites Multi-fidelity optimization via surrogate modelling,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-fidelity optimization via surrogate modelling,

Reference 39

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Observation 9dafb278-644d-4eb0-b6a7-1e1494907d91 · outbound

This paper cites A multilevel projection-based model order reduction framework for nonlinear dynamic multiscale problems in structural and solid mechanics,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A multilevel projection-based model order reduction framework for nonlinear dynamic multiscale problems in structural and solid mechanics,

Reference 40

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Observation dc802bc2-6d57-457f-8cb1-309b41381290 · outbound

This paper cites Design optimization using hyper-reduced-order mod- els,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Design optimization using hyper-reduced-order mod- els,

Reference 41

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Observation 9e559702-4f3d-4b49-ad76-ffa0fecbbb7f · outbound

This paper cites An optimization-based framework for anisotropic simplex mesh adap- tation,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data An optimization-based framework for anisotropic simplex mesh adap- tation,

Reference 42

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Observation 4a314cc5-6fc4-4b3d-83f4-d4ec9f3ef9e5 · outbound

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A statistical approach to some basic mine valuation problems on the Witwatersrand,

Reference 43

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Observation 2d5349e2-7b4d-479d-abf1-5d90cb65f71d · outbound

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Principles of geostatistics,

Reference 44

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Observation 51b00301-a26e-4c7c-8146-6b4b5e14fb99 · outbound

This paper cites Predicting the Output from a Complex Computer Code When Fast Approximations Are Available,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Predicting the Output from a Complex Computer Code When Fast Approximations Are Available,

Reference 45

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This paper cites Priors for Infinite Networks,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Priors for Infinite Networks,

Reference 46

Resolution: malformed identifier; doi_truncated, observed 2026-07-13T20:29:53.495620Z

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Observation cc107c83-2398-4cdf-858e-e871a92cd03c · outbound

This paper cites Multilayer feedforward networks are universal approx- imators,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multilayer feedforward networks are universal approx- imators,

Reference 47

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Observation 0fb54914-8310-42f8-84d9-8563beccdda9 · outbound

This paper cites Evaluation of gaussian processes and other methods for non-linear regression,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Evaluation of gaussian processes and other methods for non-linear regression,

Reference 48

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Observation cac5a23b-be99-45e9-9e81-df3ca93c865e · outbound

This paper cites Matrix formulation of co-kriging,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Matrix formulation of co-kriging,

Reference 49

Resolution: verified exact; doi, observed 2026-07-13T20:29:53.503970Z

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Observation 0bbec08f-29c4-4781-af16-773846328705 · outbound

This paper cites Linear coregionalization model: Tools for estimation and choice of cross- variogram matrix,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Linear coregionalization model: Tools for estimation and choice of cross- variogram matrix,

Reference 50

Resolution: verified exact; doi, observed 2026-07-13T20:29:53.421455Z

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Observation b0ff262f-953c-453b-b8ff-2ce7e6600a0a · outbound

This paper cites Kernels for Vector-Valued Functions: A Review,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Kernels for Vector-Valued Functions: A Review,

Reference 51

Resolution: verified exact; doi, observed 2026-07-13T20:29:53.521281Z

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Observation 0d909534-3771-42a7-ab7c-e0d769904812 · outbound

This paper cites Multi-task Gaussian Process Prediction,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-task Gaussian Process Prediction,

Reference 52

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Observation 3624f8ed-31f0-4ba6-bcef-aa8565db7bde · outbound

This paper cites Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

Reference 53

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This paper cites Kernel principal component analysis-based Gaussian process regression mod- elling for high-dimensional reliability analysis,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Kernel principal component analysis-based Gaussian process regression mod- elling for high-dimensional reliability analysis,

Reference 54

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This paper cites Variable noise and dimensionality reduction for sparse Gaussian pro- cesses,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Variable noise and dimensionality reduction for sparse Gaussian pro- cesses,

Reference 55

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation,

Reference 56

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Deep Kernel Learning

Reference 57

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Bayesian Analysis,

Reference 58

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Sparse Gaussian process hyperparam- eters: Optimize or integrate?

Reference 59

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Unresolved cited work

Reference 60

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This paper cites Available:http://link.springer.com/10.1007/978-1-4612-1494-6.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Available:http://link.springer.com/10.1007/978-1-4612-1494-6

Reference 61

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Practical Bayesian Optimization of Machine Learning Al- gorithms,

Reference 62

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This paper cites Spatial Modelling Using a New Class of Nonstationary Covariance Functions,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Spatial Modelling Using a New Class of Nonstationary Covariance Functions,

Reference 63

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Non-Gaussian Process Regression

Reference 64

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Deep Gaussian Processes,

Reference 65

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Sparse Greedy Matrix Approximation for Machine Learning,

Reference 66

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Using the Nyström Method to Speed Up Kernel Machines,

Reference 67

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Variational Learning of Inducing Variables in Sparse Gaussian Processes,

Reference 68

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Efficient SVM Training Using Low-Rank Kernel Representations,

Reference 69

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Kernel Interpolation for Scalable Structured Gaussian Processes (KISS- GP),

Reference 70

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Random Features for Large-Scale Kernel Machines,

Reference 71

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Scalable Kernel Methods via Doubly Stochastic Gradients,

Reference 72

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-fidelity Gaussian process regression for computer experiments,

Reference 73

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Multi-Fidelity for MDO Using Gaussian Pro- cesses,

Reference 74

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Deep Multi-fidelity Gaussian Processes

Reference 75

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This paper cites A combined modeling method for complex multi-fidelity data fusion,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A combined modeling method for complex multi-fidelity data fusion,

Reference 76

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A generalized hierarchical co-Kriging model for multi- fidelity data fusion,

Reference 77

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data A sequential multi-fidelity metamodeling approach for data regression,

Reference 78

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This paper cites Gaussian process fusion method for multi-fidelity data with heterogeneity distribution in aerospace vehicle flight dynamics,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Gaussian process fusion method for multi-fidelity data with heterogeneity distribution in aerospace vehicle flight dynamics,

Reference 79

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling,

Reference 80

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Deep Gaussian Processes for Multi-fidelity Modeling,

Reference 81

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Extended Co-Kriging interpolation method based on multi-fidelity data,

Reference 82

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EX- PERIMENTS WITH MULTIPLE LEVELS OF FIDELITY,

Reference 83

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Universal Kriging and Cokriging as a Regression Procedure,

Reference 84

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Active learning inspired multi-fidelity probabilistic modelling of geomaterial property,

Reference 85

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Unresolved cited work

Reference 86

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Laminar flame speed measurements of ethylene at high preheat tem- peratures and for diluted oxidizers,

Reference 87

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Lu & co-workers, 2017

Reference 88

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Zettervall,Methodology for developing reduced reaction mechanisms, and their use in combustion simula- tions, en

Reference 89

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This paper cites Dynamic Hybrid Reynolds-Averaged Navier–Stokes/Large-Eddy Simulation of a Supersonic Cavity: Chemistry Effects,.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Dynamic Hybrid Reynolds-Averaged Navier–Stokes/Large-Eddy Simulation of a Supersonic Cavity: Chemistry Effects,

Reference 90

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Observation d348b9f4-13a2-4ba8-95f1-6b78a60fa97e · outbound

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Error In Shock Tube Ignition Delay Time Predictions Due to Bifurcation and Boundary Layer Effects,

Reference 91

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Overview of a New Project for CFD Validation of Supersonic Mixing and Combustion,

Reference 92

Resolution: verified exact; doi, observed 2026-07-13T20:29:53.472794Z

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Chapter 14 Molecular Tagging Velocimetry in Gases,

Reference 93

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Reduced order modeling for high-speed flows with moving shocks,

Reference 94

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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks

Reference 95

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