Active learning with generalized sliced inverse regression for high-dimensional reliability analysis

Yin, Jianhua; Du, Xiaoping

doi:10.1016/j.strusafe.2021.102151

Cited by 23 publications

(4 citation statements)

References 49 publications

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“…The remarkable efficacy of this framework arises from the usage of an information metric (the acquisition function) to guide data acquisition. Indeed, GP models along with AL strategies have been utilized to refine predictions for functions of up to 40 variables [37]. Further, surrogate models can be used to efficiently screen input variables that do not have a discernible effect on the corrosion property of interest [38], which is especially useful when the inputs are high-dimensional.…”

Section: Active Learning Framework Drastically Improves Out-of-distri...mentioning

confidence: 99%

An Active Learning Framework for the Rapid Assessment of Galvanic Corrosion

Zapiain,

Venkatraman,

Katona

et al. 2024

Preprint

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The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately pre- dict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procur- ing the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic cou- ple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maxi- mum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

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Section: Active Learning Framework Drastically Improves Out-of-distri...mentioning

confidence: 99%

An Active Learning Framework for the Rapid Assessment of Galvanic Corrosion

Zapiain,

Venkatraman,

Katona

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The remarkable efficacy of this framework arises from the usage of an information metric (the acquisition function) to guide data acquisition. Indeed, GP models along with AL strategies have been utilized to refine predictions for functions of up to 40 variables 42 . Further, surrogate models can be used to efficiently screen input variables that do not have a discernible effect on the corrosion property of interest 43 , which is especially useful when the inputs are high-dimensional.…”

Section: Articlementioning

confidence: 99%

An active learning framework for the rapid assessment of galvanic corrosion

Venkatraman,

Katona,

Maestas

et al. 2024

npj Mater Degrad

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The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

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“…Surrogate models can be built using traditional response surface modeling (RSM) and can also be built with machine learning (ML) methods, such as Gaussian process (GP), support vector machines (SVM), and neural network. However, the computational cost is high when the dimension of the problems is high [6,7].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Convolutional Neural Network Metamodel with Image and Numerical Data

2022

AIAA SCITECH 2022 Forum

Self Cite

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Uncertainty Quantification (UQ) plays a critical role in engineering analysis and design. Regression is commonly employed to construct surrogate models to replace expensive simulation models for UQ. Classical regression methods suffer from the curse of dimensionality, especially when image data and numerical data coexist, which makes UQ computationally unaffordable. In this work, we propose a Convolutional Neural Network (CNN) based framework, which accommodates both image and numerical data. We first transform numerical data into images and then combine them with existing image data. The combined images are fed to CNN for regression. To obtain the model uncertainty, we integrate CNN with Gaussian Process (GP), which results in the mixed network CNN-GP. The simulation results show that CNN-GP can build accurate surrogate models for UQ with mixed data and that CNN-GP can also provide the uncertainty associated with the model prediction.

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Active learning with generalized sliced inverse regression for high-dimensional reliability analysis

Cited by 23 publications

References 49 publications

An Active Learning Framework for the Rapid Assessment of Galvanic Corrosion

An Active Learning Framework for the Rapid Assessment of Galvanic Corrosion

An active learning framework for the rapid assessment of galvanic corrosion

Uncertainty Quantification of Convolutional Neural Network Metamodel with Image and Numerical Data

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