Physics-Informed Neural Corrector for Deformation-based Fluid
We present a method to rectify deformed fluid flows using neural networks. Our neural corrector ensures the physical plausibility
of edited simulation footprints at test time, enabling interactive control of fluids without re-simulations.
Jingwei Tang (DisneyResearch|Studios)
Byungsoo Kim (ETH Zurich/ ETH Joint PhD)
Vinicius Azevedo (DisneyResearch|Studios)
Barbara Solenthaler (ETH Zurich)
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.