Crash simulations play an essential role in improving vehicle safety, design optimization, and injury risk estimation. Unfortunately, numerical solutions of such problems using state-of-the-art high-fidelity models require significant computational effort. Conventional data-driven surrogate modeling approaches create low-dimensional embeddings for evolving the dynamics in order to circumvent this computational effort. Most approaches directly operate on high-resolution data obtained from numerical discretization, which is both costly and complicated for mapping the flow of information over large spatial distances. Furthermore, working with a fixed resolution prevents the adaptation of surrogate models to environments with variable computing capacities, different visualization resolutions, and different accuracy requirements. We thus propose a multi-hierarchical framework for structurally creating a series of surrogate models for a kart frame, which is a good proxy for industrial-relevant crash simulations, at different levels of resolution. For multiscale phenomena, macroscale features are captured on a coarse surrogate, whereas microscale effects are resolved by finer ones. The learned behavior of the individual surrogates is passed from coarse to finer levels through transfer learning. In detail, we perform a mesh simplification on the kart model to obtain multi-resolution representations of it. We then train a graph-convolutional neural network-based surrogate that learns parameter-dependent low-dimensional latent dynamics on the coarsest representation. Subsequently, another, similarly structured surrogate is trained on the residual of the first surrogate using a finer resolution. This step can be repeated multiple times. By doing so, we construct multiple surrogates for the same system with varying hardware requirements and increasing accuracy.

翻译：碰撞仿真在提升车辆安全性、优化设计及损伤风险评估中扮演关键角色。然而，采用当前先进高保真模型求解此类问题需耗费大量计算资源。传统数据驱动代理建模方法通过创建低维嵌入来演化动力学过程以规避计算开销，但多数方法直接处理数值离散获得的高分辨率数据，这在映射大空间距离信息流时既昂贵又复杂。此外，固定分辨率工作模式阻碍了代理模型适应计算能力可变、可视化分辨率不同及精度要求各异的环境。为此，我们提出多层级框架，针对工业相关碰撞仿真的良好代理对象——卡丁车车架，系统构建一系列不同分辨率的代理模型。针对多尺度现象，粗粒度代理捕获宏观特征，细粒度代理解析微观效应，并通过迁移学习将个体代理的学习行为从粗层级传递至细层级。具体而言，我们对卡丁车模型进行网格简化以获得多分辨率表示，随后在粗网格上训练基于图卷积神经网络的代理模型，学习参数依赖的低维潜在动力学。接着，利用更细分辨率训练结构与前述代理相似的第二个代理模型，用于拟合首个代理的残差。该步骤可重复多次。通过此方法，我们为同一系统构建了多个具有不同硬件需求且精度递增的代理模型。