基于物理及数据驱动的流体动画研究  

Physically-based and Data-driven Fluid Simulation Research

作　　者：肖祥云 杨旭波[1] XIAO Xiang-Yun;YANG Xu-Bo(School of Software,Shanghai Jiaotong University,Shanghai 200240,China)

机构地区：[1]上海交通大学软件学院,上海200240

出　　处：《软件学报》2020年第10期3251-3265,共15页Journal of Software

基　　金：国家重点研发计划(2018YFB1004902);国家自然科学基金(61772329)。

摘　　要：主要针对近年来流行的基于物理及数据驱动的各种流体动画模拟算法及其应用给出了一个全面的前沿性综述.首先,对传统的基于物理的流体模拟加速方法进行了综述和总结,同时给出了此类方法中各种算法的优劣性分析;其次,对现有的基于数据驱动的多种算法进行了综述和分析.特别地,将现有的数据驱动方法归结为3类,即数据插值法、数据预计算方法和基于深度学习的方法.并且,进一步讨论了基于数据驱动的流体动画模拟算法的几个关键问题以及其研究趋势与方向.This paper gives a comprehensive overview of the popular animation simulation algorithms based on physical and data-driven methods as well as their applications in recent years.Firstly,the traditional physically-based acceleration fluid simulation methods are summarized,covering both their advantages and disadvantages.Then,the existing data-driven algorithms applied in fluid simulation are summarized and analyzed.In particular,the existing data-driven methods are sumed up into three types,namely,interpolation methods,methods based on pre-computed data,and deep learning methods.Further,some key points are given about the data-driven methods as well as the research trends and directions.

关 键 词：流体动画模拟 基于物理的动画 模拟加速 数据驱动 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]