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Physics-Informed Neural Corrector for Deformation-based Fluid Control

Jingwei Tang, Byungsoo Kim, Vinicius Azevedo, Barbara Solenthaler

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 them leading that they lead to physical and eventually visually appealing modifications on edited fluid data.

Physics-Informed Neural Corrector for Deformation-based Fluid Control

Posted by christopherbatty on June 4th, 2023 Posted in Uncategorized
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