MPMNet: A Data-Driven MPM Framework for Dynamic Fluid-Solid Interaction
PubDate: May 2023
Teams: Beihang University;Stony Brook University
Writers: Jin Li, Yang Gao, Ju Dai, Shuai Li, Aimin Hao, Hong Qin
PDF: MPMNet: A Data-Driven MPM Framework for Dynamic Fluid-Solid Interaction
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.