Using a Gaussian process regression inspired method to measure agreement between the experiment and CFD simulations

Duan, Yu; Cooling, C.M.; Ahn, Ji Soo; Jackson, Christopher; Flint, Adam; Eaton, M.D.; Bluck, Michael J.

doi:10.1016/j.ijheatfluidflow.2019.108497

Cited by 25 publications

(16 citation statements)

References 42 publications

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“…In the last decade, the GPR model has attracted considerable attention, especially in ML approaches [ 32 ]. These methods apply non-parametric kernel functions based on probabilistic models (Bayesian inference) [ 20 ]. These non-parametric methods are usually more rigorous than the standard regression methods described above, especially for the treatment of complex and noisy non-linear functions [ 33 ] and its cross validation [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

“…The evaluation of the models can be implemented by assessing the difference between the observed values (

) and predicted values (

) [ 20 ]. The performance of the regression learning models can be evaluated using classical performance results [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

“…ANN and support vector machines (SVM) models have also been applied for coating thickness estimation [ 19 ]. Regression learner using the Gaussian process regression model (GPR) has been applied to results of different computational fluid-dynamic simulations, interpolating the positions where experimental data were unknown [ 20 ]. Thermography has also been combined with deep learning (DL) strategies to detect cracks in steel [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Rodríguez‐Martín

Fueyo

Aguilera

et al. 2020

Sensors

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The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

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Section: Methodsmentioning

confidence: 99%

“…The evaluation of the models can be implemented by assessing the difference between the observed values (

) and predicted values (

) [ 20 ]. The performance of the regression learning models can be evaluated using classical performance results [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Rodríguez‐Martín

Fueyo

Aguilera

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Except for 𝐶 𝑠 , other settings, such as boundary conditions and numerical set-up, are the same in these simulations. Further details about how the simulations are set-up can be found in [4]. The chosen 𝐶 𝑠 are 0.0, 0.1, …, 1.0.…”

Section: Standard Bayesian Calibration (Std-bc) Revisitedmentioning

confidence: 99%

“…By this definition, it is natural to calibrate the model parameters using the best-fit principle. However, the best-fit principle does not consider the uncertainties that the data may be subjected to, such as the observational/numerical discretisation errors, which should be considered in determining the agreement between two databases [1][2][3][4]. Kennedy and O'Hagan [5] proposed a Bayesian approach to calibrating the computational model by representing model bias and computer model outputs as the Gaussian processes.…”

Section: Introductionmentioning

confidence: 99%

Fixed inducing points online Bayesian calibration for computer models with an application to a scale-resolving CFD simulation

Duan

Eaton

Bluck

2021

Journal of Computational Physics

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Nonlinear response modelling of material systems using constrained Gaussian processes

Herath,

Chakraborty

2024

Numerical Meth Engineering

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This article investigates the suitability of constrained Gaussian process regression in predicting nonlinear mechanical responses of material systems with notably reduced uncertainties. This study reinforces the conventional Gaussian processes with mechanics‐informed prior knowledge observed in various kinematic responses. Stiffening and softening responses of material systems mostly demonstrate at least one of the boundedness, monotonicity and convexity conditions with respect to some kinematic variables. These relationships or impositions in turn are encoded into a constrained Gaussian process for prediction, uncertainty quantification and extrapolation. Using numerous examples and comparative studies, this article evidently proves that the use of constrained Gaussian processes is data‐efficient, highly accurate, yields low uncertainties, recovers model overfitting and extrapolates very well compared to unconstrained or conventional Gaussian processes. Moreover, the usability of the proposed numerical method across various engineering modelling domains such as multiscale homogenisation, experimentation, structural optimisation, material constitutive modelling and structural idealisation is demonstrated.

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Using a Gaussian process regression inspired method to measure agreement between the experiment and CFD simulations

Cited by 25 publications

References 42 publications

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Fixed inducing points online Bayesian calibration for computer models with an application to a scale-resolving CFD simulation

Nonlinear response modelling of material systems using constrained Gaussian processes

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