Granular media surround us, comprising everything from the ground we walk on to the foods we eat. Owing to their ubiquity our ability to understand and predict the mechanical evolution of grains is not only of key scientific importance, but is also a key component to synthesize believable animations of our world. Despite their importance, shortcomings persist in our ability to simulate granular media. In particular, simulating grains with non-convex shapes remains a challenging and computationally expensive task. We propose a method to simulate non-convex rigid grains by posing geometric contact in configuration space and learning the resulting contact map with a neural network. Our formulation reduces the complex task of modeling and simulating non-convex shapes to simple network evaluations that are easily run on standard compute hardware, allowing us to quickly and robustly simulate large scale systems of non-convex grains.

翻译：颗粒介质无处不在，从我们行走的地面到我们食用的食物皆由其构成。鉴于其普遍性，我们理解和预测颗粒力学演化的能力不仅具有关键科学意义，也是合成逼真世界动画的核心要素。尽管颗粒介质至关重要，我们在模拟其行为方面仍存在不足。特别是模拟非凸形状颗粒依然是一项计算成本高昂的挑战性任务。本文提出一种模拟非凸刚性颗粒的方法：通过在构型空间中构建几何接触问题，并利用神经网络学习相应的接触映射。我们的方法将建模和模拟非凸形状的复杂任务简化为可在标准计算硬件上快速执行的简单网络评估，从而能够快速鲁棒地模拟大规模非凸颗粒系统。