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Accelerating Liquid Simulation With an Improved Data‐Driven Method
Author(s) -
Gao Yang,
Zhang Quancheng,
Li Shuai,
Hao Aimin,
Qin Hong
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14010
Subject(s) - bottleneck , computer science , animation , artificial neural network , solver , projection (relational algebra) , pipeline (software) , computer graphics , representation (politics) , set (abstract data type) , boundary (topology) , visualization , algorithm , graphics , function (biology) , computer graphics (images) , computational science , artificial intelligence , mathematics , mathematical analysis , politics , political science , embedded system , law , programming language , evolutionary biology , biology
In physics‐based liquid simulation for graphics applications, pressure projection consumes a significant amount of computational time and is frequently the bottleneck of the computational efficiency. How to rapidly apply the pressure projection and at the same time how to accurately capture the liquid geometry are always among the most popular topics in the current research trend in liquid simulations. In this paper, we incorporate an artificial neural network into the simulation pipeline for handling the tricky projection step for liquid animation. Compared with the previous neural‐network‐based works for gas flows, this paper advocates new advances in the composition of representative features as well as the loss functions in order to facilitate fluid simulation with free‐surface boundary. Specifically, we choose both the velocity and the level‐set function as the additional representation of the fluid states, which allows not only the motion but also the boundary position to be considered in the neural network solver. Meanwhile, we use the divergence error in the loss function to further emulate the lifelike behaviours of liquid. With these arrangements, our method could greatly accelerate the pressure projection step in liquid simulation, while maintaining fairly convincing visual results. Additionally, our neutral network performs well when being applied to new scene synthesis even with varied boundaries or scales.