Numerical Coarsening with Neural Shape Functions

dc.contributor.authorNi, Ningen_US
dc.contributor.authorXu, Qingyuen_US
dc.contributor.authorLi, Zhehaoen_US
dc.contributor.authorFu, Xiao‐Mingen_US
dc.contributor.authorLiu, Ligangen_US
dc.contributor.editorHauser, Helwig and Alliez, Pierreen_US
dc.date.accessioned2023-10-06T11:58:53Z
dc.date.available2023-10-06T11:58:53Z
dc.date.issued2023
dc.description.abstractWe propose to use nonlinear shape functions represented as neural networks in numerical coarsening to achieve generalization capability as well as good accuracy. To overcome the challenge of generalization to different simulation scenarios, especially nonlinear materials under large deformations, our key idea is to replace the linear mapping between coarse and fine meshes adopted in previous works with a nonlinear one represented by neural networks. However, directly applying an end‐to‐end neural representation leads to poor performance due to over‐huge parameter space as well as failing to capture some intrinsic geometry properties of shape functions. Our solution is to embed geometry constraints as the prior knowledge in learning, which greatly improves training efficiency and inference robustness. With the trained neural shape functions, we can easily adopt numerical coarsening in the simulation of various hyperelastic models without any other preprocessing step required. The experiment results demonstrate the efficiency and generalization capability of our method over previous works.en_US
dc.description.number6
dc.description.sectionheadersORIGINAL ARTICLES
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14736
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14736
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14736
dc.publisher© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.subjectanimation
dc.subjectnumercial coarsening
dc.subjectphysically based animation
dc.titleNumerical Coarsening with Neural Shape Functionsen_US
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