High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality modeling and computer animation in online games or real-time Virtual Reality (VR) systems. However, the large-scale simulation of incompressible fluid and its interaction with the surrounding solid environment is either time-consuming or suffering from the reduced time/space resolution due to the complicated iterative nature pertinent to numerical computations of involved Partial Differential Equations (PDEs). In recent years, we have witnessed significant growth in exploring a different, alternative data-driven approach to addressing some of the existing technical challenges in conventional model-centric graphics and animation methods. This paper showcases some of our exploratory efforts in this direction. One technical concern of our research is to address the central key challenge of how to best construct the numerical solver effectively and how to best integrate spatiotemporal/dimensional neural networks with the available MPM's pressure solvers.

翻译：高精度、高效率的基于物理的流固交互对于在线游戏或实时虚拟现实系统中的真实感建模和计算机动画至关重要。然而，由于涉及偏微分方程数值计算的复杂迭代特性，不可压缩流体的大规模模拟及其与周围固体环境的交互要么计算耗时，要么受限于时间/空间分辨率的降低。近年来，探索另一种替代性数据驱动方法以解决传统以模型为中心的图形学和动画方法中现有技术挑战的研究取得了显著增长。本文展示了我们在该方向上的探索性成果。本研究的一个技术重点是解决核心关键挑战：如何有效构建数值求解器，以及如何最优地将时空/维度神经网络与可用的MPM压力求解器相结合。