Physics-Informed Neural Corrector for Deformation-based Fluid Control

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dc.contributor.authorTang, Jingweien_US
dc.contributor.authorKim, Byungsooen_US
dc.contributor.authorAzevedo, Vinicius C.en_US
dc.contributor.authorSolenthaler, Barbaraen_US
dc.contributor.editorMyszkowski, Karolen_US
dc.contributor.editorNiessner, Matthiasen_US
dc.date.accessioned2023-05-03T06:10:03Z
dc.date.available2023-05-03T06:10:03Z
dc.date.issued2023
dc.description.abstractControlling 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.en_US
dc.description.number2
dc.description.sectionheadersLearning Deformations and Fluids
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14751
dc.identifier.issn1467-8659
dc.identifier.pages161-173
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14751
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14751
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Physical Simulation; Neural networks
dc.subjectComputing methodologies
dc.subjectPhysical Simulation
dc.subjectNeural networks
dc.titlePhysics-Informed Neural Corrector for Deformation-based Fluid Controlen_US
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