works on

From the 1 of 7 linked papers with an AI index.

most citedA graph neural network surrogate model for mesh-based crashworthiness prediction of vehicle panel components

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE2026

PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization

Huihao Jing, Wenbin Hu, Shaojin Chen +5

The paper introduces PerfCodeBench, an executable benchmark that evaluates how well large language models can generate system-level code that is not only correct but also optimized…

cs.LG2026

Reinforcement Learning with a Bilevel World-Model Architecture for Scan-Order Optimisation in Laser Directed Energy Deposition

Xian Wu, Haoran Li, Yuanqi Chu +2

Scan-order design in laser directed energy deposition (LDED) is a delayed, path-dependent thermo-mechanical decision problem, because sequence quality becomes observable only after…

cs.LG2026

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…

eess.SY20261 cited

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…

cs.CE2026

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 engineering analysis, but repeated simulations remain costly during design space exploration…

cs.LG2026

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…