Recently, many mesh-based graph neural network (GNN) models have been
proposed for modeling complex high-dimensional physical systems. Remarkable
achievements have been made in significantly reducing the solving time compared
to traditional numerical solvers. These methods are typically designed to i)
reduce the computational cost in solving physical dynamics and/or ii) propose
techniques to enhance the solution accuracy in fluid and rigid body dynamics.
However, it remains under-explored whether they are effective in addressing the
challenges of flexible body dynamics, where instantaneous collisions occur
within a very short timeframe. In this paper, we present Hierarchical Contact
Mesh Transformer (HCMT), which uses hierarchical mesh structures and can learn
long-range dependencies (occurred by collisions) among spatially distant
positions of a body -- two close positions in a higher-level mesh corresponds
to two distant positions in a lower-level mesh. HCMT enables long-range
interactions, and the hierarchical mesh structure quickly propagates collision
effects to faraway positions. To this end, it consists of a contact mesh
Transformer and a hierarchical mesh Transformer (CMT and HMT, respectively).
Lastly, we propose a flexible body dynamics dataset, consisting of trajectories
that reflect experimental settings frequently used in the display industry for
product designs. We also compare the performance of several baselines using
well-known benchmark datasets. Our results show that HCMT provides significant
performance improvements over existing methods.

本文介绍了一种使用层级网格结构的接触网格变换器（HCMT），该变换器能够学习身体空间上远离的位置之间的长程依赖性，并且能够快速将碰撞效应传播到远方位置。通过对多个基准数据集的性能比较，结果表明 HCMT 相比现有方法能够显著提供性能改进。