发表机构
Imperial College London; Keysight Technologies; Prometheus（伦敦帝国理工学院; 是德科技; Prometheus）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出边界条件感知Transformer神经算子BAT-NO，融合循环网格与傅里叶算子，在B柱仿真中实现几何与边界变化下的耐撞性预测，误差降低32.6%。
AI 中文摘要
高保真有限元模拟能够提供准确的耐撞性预测，但其计算成本限制了迭代设计探索。深度学习代理模型可以降低这一成本，但许多部件级模型是在单一指定边界条件下开发的，限制了其对边界条件变化的泛化能力。本文提出了一种边界条件感知Transformer神经算子（BAT-NO），用于在几何和边界条件变化下自回归预测瞬态位移场和标量耐撞性响应。一个B柱仿真框架评估了在几何、冲击位置和速度以及支撑刚度变化下的泛化能力。BAT-NO将循环网格处理与潜在网格傅里叶算子处理相结合。边界条件信息通过一种混合局部-全局机制传递到潜在网格。基于切片的注意力机制建模物理相关区域之间的相互作用，而直接的边界到网格投影则保留了局部空间结构。在仅形状、形状与载荷以及形状-载荷-边界三种验证集上，BAT-NO在评估的基线模型中取得了最低的平均最终步节点欧氏位移误差。在最具挑战性的情况下，其平均误差相对于次优模型降低了32.6%。超参数调优将验证误差从0.451毫米降至0.269毫米，在调查设计空间内采样的300个未见测试模拟上获得了0.267毫米的相当误差。一个基于注意力的标量解码器联合预测六条响应轨迹，平均相对误差为2.46%。大多数导出的耐撞性指标的误差中位数低于3%。这些结果表明，显式的局部和全局边界条件表示能够改善扩展部件级设计空间上的耐撞性预测。
英文摘要
High-fidelity finite-element simulations provide accurate crashworthiness predictions, but their cost limits iterative design exploration. Deep learning surrogates can reduce this cost, but many component-level models are developed under a single prescribed boundary condition, limiting generalisation to boundary variations. This work proposes a Boundary-Condition-Aware Transformer Neural Operator (BAT-NO) for autoregressive prediction of transient displacement fields and scalar crashworthiness responses under variations in geometry and boundary conditions. A B-pillar simulation framework evaluates generalisation across variations in geometry, impact position and velocity, and support stiffness. BAT-NO combines recurrent mesh processing with latent-grid Fourier operator processing. Boundary-condition information is transferred to the latent grid through a hybrid local--global mechanism. Slice-based attention models interactions among physically related regions, while direct boundary-to-grid projection preserves local spatial structure. Across the validation sets for the shape-only, shape-and-loading, and shape-loading-boundary cases, BAT-NO achieves the lowest mean final-step mean nodal Euclidean displacement error among the evaluated baselines. In the most challenging case, it reduces the mean error by 32.6% relative to the second-best model. Hyperparameter tuning reduces the validation error from 0.451 to 0.269 mm, with a comparable error of 0.267 mm on 300 unseen test simulations sampled within the investigated design space. An attention-based scalar decoder jointly predicts six response trajectories with a mean relative error of 2.46%. Most derived crashworthiness indicators have median errors below 3%. These results show that explicit local and global boundary-condition representations improve crashworthiness prediction over expanded component-level design spaces.
