arXiv:2606. 10909v1 Announce Type: cross Abstract: Reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading remains a major computational bottleneck in multi-scale simulations.
By Manuel Ricardo Guevara Garban, Yves Chemisky, \'Etienne Pruli\`ere, Micha\"el Cl\'ement, Martin Abendroth, Bj\"orn Kiefer
arXiv:2609.26426v1 Announce Type: new
Abstract: Finite Element Analysis (FEA) is widely used for transient mechanical simulations, but its high computational cost limits real-time and high-resolution...
By Georgios Triantafyllou, Panagiotis G. Kalozoumis, Dimitris K. Iakovidis
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:2609.14977v1 Announce Type: cross
Abstract: Finite element stress fields often exhibit strong local non-smoothness, where stress concentrations near holes, notches, and loading regions induce s...
By Chen Zeng, Qiao Wang
arXiv:2503. 17386v2 Announce Type: replace-cross Abstract: Crashworthiness is a key performance measure in the design of safety-critical vehicle panel components such as B-pillars.
By Haoran Li, Yingxue Zhao, Haosu Zhou, Tobias Pfaff, Nan Li
arXiv:2606. 19378v1 Announce Type: new Abstract: Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge.
By Hyeonbin Moon, Yongjin Choi, Seunghwa Ryu