1 citations · 1 across the 6 of their papers we have counts for
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BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components
Haoran Li, Yingxue Zhao, Haosu Zhou +4
High-fidelity finite-element simulations provide accurate crashworthiness predictions, but their cost limits iterative design exploration. Deep learning surrogates can reduce this…
Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation
Haoran Li, Tobias Lehrer, Yingxue Zhao +5
Nonlinear finite element crash simulations are accurate but computationally expensive, limiting their use in iterative design optimisation. Machine-learning surrogate models based…
A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming
Yingxue Zhao, Haoran Li, Haosu Zhou +2
Explicit dynamic finite element (FE) simulations are widely used for large-deformation analysis but remain expensive for design-space exploration. In explicit FE analysis, nodal ki…
StampFormer: A Physics-Guided Material-Geometry-Coupled Multimodal Model for Rapid Prediction of Physical Fields in Sheet Metal Stamping
Jiajie Luo, Mohamed Mohamed, Osama Hassan +8
Traditional sheet metal forming relies on time-consuming and expensive Finite Element Analysis (FEA) for design validation, a process that significantly prolongs design cycles. Whi…