Afsal Pulikkathodi, Ludovic Chamoin, Elisabeth Lacazedieu, Juan Pedro Berro Ramirez, Laurent Rota, Malek Zarroug
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引用次数: 0
Abstract
The article tackles the challenge of effective modeling and simulation of large mechanical structures exhibiting numerous local complex behaviors, as encountered with spot welds in automotive crash numerical analysis. To address this challenge, we propose a nonintrusive local/global coupling strategy, where the local model is a neural network-based reduced model, specifically a physics-guided neural network (PGANN). This multiscale strategy enables accurate modeling of complex localized behaviors while maintaining computational efficiency, without modifying the global solver. The proposed approach is validated through a series of structural examples, including full 3D industrial structures with multiple spot welds.
期刊介绍：
The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems.
The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.
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