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、设计优化和损伤风险评估中具有重要作用。然而，利用前沿高保真模型对此类问题进行数值求解需要巨大的计算成本。传统数据驱动的代理建模方法通过创建低维嵌入来演化动力学过程，以规避这一计算负担。大多数方法直接处理数值离散化生成的高分辨率数据，这不仅计算成本高昂，且难以映射大空间距离上的信息流动。此外，固定分辨率的使用阻碍了代理模型适应不同计算能力、可视化精度及准确性要求的应用场景。为此，我们提出一种多层级框架，以结构化的方式为工业相关碰撞仿真的典型代表——卡丁车车架——构建一系列不同分辨率下的代理模型。针对多尺度现象，粗粒度代理捕获宏观特征，而细粒度代理解析微观效应。通过迁移学习，各代理模型习得的行为从粗粒度层级向细粒度层级传递。具体而言，我们对卡丁车模型进行网格简化以获得其多分辨率表示，然后基于最粗分辨率训练图卷积神经网络代理，使其学习参数相关的低维潜在动力学。随后，在第一个代理的残差基础上，采用更高分辨率训练另一个结构相似的代理。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硬件需求各异且精度逐级提升的代理模型。