A Small-Sample Graph Neural Network Approach for Predicting Sortie Mission Reliability of Shipborne Vehicle Layouts

Conventional methods for calculating sortie mission reliability of shipborne vehicle layouts suffer from excessive computational overhead, long runtimes, and large labeled data requirements. To address these limitations, this work proposes a specialized graph neural network architecture tailored for...

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Detalles Bibliográficos

Autores principales:	Han Shi, Nengjian Wang, Qinhui Liu
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2026-03-01
Colección:	Journal of Marine Science and Engineering
Materias:	small-sample learning graph neural networks shipborne vehicles layouts reliability prediction
Acceso en línea:	https://www.mdpi.com/2077-1312/14/7/599
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