MeshGraphNet-Transformer (MGN‑T) is a new architecture that fuses Transformers’ global modeling with MeshGraphNets’ geometric inductive bias, keeping a mesh‑based graph representation. It replaces iterative message passing with a physics‑attention Transformer that updates all nodal states simultaneously, enabling efficient learning on high‑resolution meshes with diverse geometries, topologies, and boundary conditions. MGN‑T accurately models impact dynamics, self‑contact, plasticity, and multivariate outputs, outperforming state‑of‑the‑art methods on classical benchmarks while using far fewer parameters.
By Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez, Elias Cueto
arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.
By Jaeyeong Lee, Taeseong Yoon, Wonmo Koo, Heeyoung Kim
arXiv:2306.11313v5 Announce Type: replace-cross
Abstract: Point process models are widely used for continuous-time discrete-event data, where each data point includes time and additional information...
By Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie
arXiv:2502. 16533v3 Announce Type: replace-cross Abstract: Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-smoothing and over-squashing.
By Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, Liang Wang, Tingyang Xu, Wenbing Huang, Deli Zhao, Hong Cheng, Yu Rong
arXiv:2607. 24213v1 Announce Type: new Abstract: Integrating heterogeneous industrial knowledge, including factual relations and decision constraints, remains a core challenge in industrial information systems.
By Yuntong Chen, Yingqi Li, Yingying Xiao, Ziang Wang, Zewei Liu, Jiahao Liu, Xitian Tian, Lijiang Huang
arXiv:2610.02517v1 Announce Type: new
Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection an...
By Yu Song, Zhigang Hua, Yan Xie, Bingheng Li, Jingzhe Liu, Bo Long, Jiliang Tang, Hui Liu