1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming
Yingxue Zhao, Qianyi Chen, Haoran Li +5
In recent years, various artificial intelligence-based surrogate models have been proposed to provide rapid manufacturability predictions of material forming processes. However, tr…
A graph neural network surrogate model for mesh-based crashworthiness prediction of vehicle panel components
Haoran Li, Yingxue Zhao, Haosu Zhou +2
Crashworthiness is a key performance measure in the design of safety-critical vehicle panel components such as B-pillars. Finite element (FE) simulations are widely used to evaluat…
A review of graph neural network applications in mechanics-related domains
Yingxue Zhao, Haoran Li, Haosu Zhou +3
Mechanics-related problems often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform structures. Graph neural netwo…