arXiv AI

Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks

arXiv:2607. 01522v1 Announce Type: cross Abstract: Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engineers.

Hugging Face Trending Papers
Jul 13

A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries

Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data.

arXiv Machine Learning
Jun 19

Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges

arXiv:2606. 20015v1 Announce Type: new Abstract: Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twins.

By Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly, Tarek Abdoun, Mostafa E. Mobasher
arXiv Machine Learning
Jun 18

A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming

arXiv:2605. 22845v2 Announce Type: replace-cross Abstract: Explicit dynamic finite element (FE) simulations are widely used for large deformation engineering analysis, but repeated simulations remain costly during design space exploration and optimisation.

By Yingxue Zhao, Haoran Li, Haosu Zhou, Tobias Pfaff, Nan Li
arXiv Machine Learning
Jun 10

Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

arXiv:2606. 10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions induced by repeated layer-wise melting, solidification, and reheating across the 3D build.

By Joyce Karen Pelaez, Siqi Zhang, Hoo Sang Ko
arXiv Machine Learning
Jun 3

LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation

arXiv:2510. 22491v3 Announce Type: replace Abstract: Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide reliable control beyond the training distribution.

By Ghadi Nehme, Yanxia Zhang, Dule Shu, Matt Klenk, Faez Ahmed