Graph Neural Networks (GNNs) have recently been explored as surrogate models for numerical simulations. While their applications in computational fluid dynamics have been investigated, little attention has been given to structural problems, especially for dynamic cases. To address this gap, we introduce the Graph Network-based Structural Simulator (GNSS), a GNN framework for surrogate modeling of dynamic structural problems. GNSS follows the encode-process-decode paradigm typical of GNN-based machine learning models, and its design makes it particularly suited for dynamic simulations thanks to three key features: (i) expressing node kinematics in node-fixed local frames, which avoids catastrophic cancellation in finite-difference velocities; (ii) employing a sign-aware regression loss, which reduces phase errors in long rollouts; and (iii) using a wavelength-informed connectivity radius, which optimizes graph construction. We evaluate GNSS on a case study involving a beam excited by a 50kHz Hanning-modulated pulse. The results show that GNSS accurately reproduces the physics of the problem over hundreds of timesteps and generalizes to unseen loading conditions, where existing GNNs fail to converge or deliver meaningful predictions. Compared with explicit finite element baselines, GNSS achieves substantial inference speedups while preserving spatial and temporal fidelity. These findings demonstrate that locality-preserving GNNs with physics-consistent update rules are a competitive alternative for dynamic, wave-dominated structural simulations.

翻译：图神经网络（GNNs）近年来被探索作为数值模拟的替代模型。尽管其在计算流体动力学中的应用已得到研究，但针对结构问题，尤其是动态工况的关注较少。为填补这一空白，我们提出了基于图网络的结构模拟器（GNSS），一种用于动态结构问题替代建模的GNN框架。GNSS遵循基于GNN的机器学习模型典型的编码-处理-解码范式，其设计通过三个关键特性使其特别适用于动态模拟：（i）在节点固定局部坐标系中表达节点运动学，避免了有限差分速度中的灾难性抵消；（ii）采用符号感知回归损失，减少了长序列推演中的相位误差；（iii）使用波长感知的连接半径，优化了图构建。我们在一个涉及50kHz汉宁调制脉冲激励梁的案例研究中评估GNSS。结果表明，GNSS在数百个时间步长内准确复现了问题的物理特性，并能泛化到未见过的加载条件，而现有GNNs无法收敛或提供有意义的预测。与显式有限元基线相比，GNSS在保持空间和时间保真度的同时实现了显著的推理加速。这些发现表明，具有物理一致更新规则的局部保持GNNs是动态、波主导结构模拟的有竞争力的替代方案。