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Communication Dans Un Congrès Année : 2022

Modeling complex mechanical computer codes with functional input via Gaussian processes

Résumé

Surrogate models based on Gaussian processes (GPs) have been successfully used as a complement of costly-to-evaluate complex computer codes. They are capable to provide accurate predictions with confident intervals but require fewer costs (in both time and resources). Here, we focus on a class of mechanical codes with functional inputs and output force-displacement curves. We further investigate a GP framework where inputs are handled in a continuous setting, which results in more tractable and scalable models. Both input and output information are correlated using a composite kernel function that can be efficiently computed (and inverted) when tensor-structured data are considered. We demonstrate the reliability and scalability of the GP in a synthetic example with highly variable input and output curves, as well as on a real-world mechanical application modeling the self-piercing riveting (SPR) in a single hat component. Our experiments show that the methodology is able to correctly detect the maximum forces and the displacements at peak force where the failure of SPR appears.
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Dates et versions

hal-03882473 , version 1 (19-12-2022)

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  • HAL Id : hal-03882473 , version 1

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Andrés F. López-Lopera, Franck Massa, Isabelle Turpin, Nicolas Leconte. Modeling complex mechanical computer codes with functional input via Gaussian processes. The 43th Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE), Nov 2022, Foz do Iguaçu, Brazil. ⟨hal-03882473⟩
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