Accelerating Liquid Simulation With an Improved Data‐Driven Method

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dc.contributor.authorGao, Yangen_US
dc.contributor.authorZhang, Quanchengen_US
dc.contributor.authorLi, Shuaien_US
dc.contributor.authorHao, Aiminen_US
dc.contributor.authorQin, Hongen_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-10-06T16:54:00Z
dc.date.available2020-10-06T16:54:00Z
dc.date.issued2020
dc.description.abstractIn 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.en_US
dc.description.number6
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14010
dc.identifier.issn1467-8659
dc.identifier.pages180-191
dc.identifier.urihttps://doi.org/10.1111/cgf.14010
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14010
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectphysically based animation
dc.subjectanimation
dc.titleAccelerating Liquid Simulation With an Improved Data‐Driven Methoden_US
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