Material Point Method-Based Simulation Techniques for Medical Applications

Abstract

We propose a method for recognizing fragment objects to model the detailed tearing of elastic objects like human organs. Traditional methods require high-performance GPUs for real-time calculations to accurately simulate the detailed fragmentation of rapidly deforming objects or create random fragments to improve visual effects with minimal computation. The proposed method utilizes a deep neural network (DNN) to produce physically accurate results without requiring high-performance GPUs. Physically parameterized material point method (MPM) simulation data were used to learn small-scale detailed fragments. The tearing process is segmented and learned based on various training data from different spaces and external forces. The inference algorithm classifies the fragments from the training data and modifies the deformation gradient using a modifier. An experiment was conducted to compare the proposed method and the traditional MPM in the same environment. As a result, it was confirmed that visual fidelity for the tearing of elastic objects has been improved. This supports the simulation of various incision types in a virtual surgery.