Physics-based simulation of mesh based domains remains a challenging task. State-of-the-art techniques can produce realistic results but require expert knowledge. A major bottleneck in many approaches is the step of integrating a potential energy in order to compute velocities or displacements. Recently, learning based method for physics-based simulation have sparked interest with graph based approaches being a promising research direction. One of the challenges for these methods is to generate models that are mesh independent and generalize to different material properties. Moreover, the model should also be able to react to unforeseen external forces like ubiquitous collisions. Our contribution is based on a simple observation: evaluating forces is computationally relatively cheap for traditional simulation methods and can be computed in parallel in contrast to their integration. If we learn how a system reacts to forces in general, irrespective of their origin, we can learn an integrator that can predict state changes due to the total forces with high generalization power. We effectively factor out the physical model behind resulting forces by relying on an opaque force module. We demonstrate that this idea leads to a learnable module that can be trained on basic internal forces of small mesh patches and generalizes to different mesh typologies, resolutions, material parameters and unseen forces like collisions at inference time. Our proposed paradigm is general and can be used to model a variety of physical phenomena. We focus our exposition on the detail enhancement of coarse clothing geometry which has many applications including computer games, virtual reality and virtual try-on.

翻译：基于网格域的物理仿真仍是一项具有挑战性的任务。现有先进技术虽能生成逼真结果，但需要专家知识。许多方法的主要瓶颈在于通过势能积分计算速度或位移的步骤。近年来，基于学习方法的物理仿真引起了广泛关注，其中图方法成为一个有前景的研究方向。这些方法面临的挑战之一是如何生成网格无关且能泛化至不同材料属性的模型。此外，模型还需能应对如普遍存在的碰撞等未知外力。我们的贡献基于一个简单观察：传统仿真方法中力的计算相对廉价且可并行处理，而积分步骤则不然。通过学习系统对外力（无论其来源如何）的通用响应机制，我们能够训练一个积分器，该积分器可基于总外力预测状态变化，并具有高度泛化能力。我们通过引入一个不透明力模块，有效解耦了产生力的物理模型。实验证明，该思路可生成可学习模块，该模块仅需在小网格补片上训练基本内力，即可在推理时泛化至不同网格拓扑结构、分辨率、材料参数以及碰撞等未知外力。我们提出的范式具有通用性，可模拟多种物理现象。本文着重阐述其在粗糙服装几何细节增强中的应用，该技术可广泛应用于计算机游戏、虚拟现实及虚拟试穿等领域。