Full-vehicle crash simulations are computationally expensive, limiting their use in iterative design exploration. This work investigates learned hybrid surrogate models (MeshTransolver, MeshGeoTransolver, and MeshGeoFLARE) for predicting time-resolved structural deformation fields in an industrial lateral pole-impact benchmark. We evaluate whether neural surrogates can reproduce full-field crash kinematics with sufficient accuracy, spatial regularity, and structural plausibility for engineering interpretation. The proposed architectures combine local mesh message passing, geometry-aware global attention, and sparse contact-aware correction for autoregressive crash rollout. We compare mesh-based graph neural networks, attention-based geometric models, and hybrid architectures under a common training and hyperparameter configuration. The hybrid models capture both short-range structural interactions and long-range deformation patterns, while a sparse contact-aware variant assesses the effect of dynamic proximity interactions during rollout. On a 25-sample full-vehicle test set, the best hybrid model achieves a temporal mean root-mean-square error of 3.20 mm. While geometry-aware attention baselines are quantitatively competitive, qualitative side-view inspection shows they can introduce local spatial noise and deformation irregularities that complicate structural interpretation. In contrast, hybrid mesh-attention models provide the best balance between scalar accuracy, survival-space consistency, and physically interpretable displacement fields. These results suggest that crash surrogate assessment should combine global error metrics with downstream safety-relevant quantities and qualitative field inspection. The proposed methodology enables fast full-field predictions while preserving essential structural information for industrial crash-engineering analysis.

翻译：整车碰撞仿真计算成本高昂，限制了其在迭代设计探索中的应用。本研究探讨了学习型混合代理模型（MeshTransolver、MeshGeoTransolver 和 MeshGeoFLARE）在工业侧柱碰撞基准测试中预测时变结构变形场的能力。我们评估了神经代理模型能否以足够的精度、空间规律性和结构合理性再现全场碰撞运动学响应，以满足工程解析需求。所提出的架构结合了局部网格消息传递、几何感知全局注意力机制以及稀疏接触感知校正，用于自回归碰撞推演。我们在统一的训练与超参数配置下，比较了基于网格的图神经网络、基于注意力的几何模型及混合架构。混合模型既能捕捉短程结构相互作用，也能捕获长程变形模式；同时，稀疏接触感知变体评估了推演过程中动态邻近相互作用的影响。在包含25个样本的整车测试集上，最佳混合模型实现了3.20毫米的时间均方根误差。尽管几何感知注意力基线在定量指标上具有竞争力，但侧面视图的定性检查显示，它们可能引入局部空间噪声和变形不规则性，从而干扰结构解析。相比之下，混合网格-注意力模型在标量精度、生存空间一致性和物理可解释的位移场之间实现了最佳平衡。这些结果表明，碰撞代理模型评估应将全局误差指标与下游安全相关量及定性场检查相结合。所提出的方法在保留工业碰撞工程分析关键结构信息的同时，实现了快速全场预测。