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Date
2023-05Type
- Journal Article
Abstract
Controlling fluid simulations is notoriously difficult due to its high computational cost and the fact that user control inputs can cause unphysical motion. We present an interactive method for deformation-based fluid control. Our method aims at balancing the direct deformations of fluid fields and the preservation of physical characteristics. We train convolutional neural networks with physics-inspired loss functions together with a differentiable fluid simulator, and provide an efficient workflow for flow manipulations at test time. We demonstrate diverse test cases to analyze our carefully designed objectives and show that they lead to physical and eventually visually appealing modifications on edited fluid data. Show more
Publication status
publishedExternal links
Journal / series
Computer Graphics ForumVolume
Pages / Article No.
Publisher
Wiley-BlackwellSubject
CCS Concepts; Physical Simulation; Neural networksOrganisational unit
08736 - Solenthaler, Barbara (Tit.-Prof.) / Solenthaler, Barbara (Tit.-Prof.)
03420 - Gross, Markus / Gross, Markus
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