filename     : Tan23a.pdf
entry        : inproceedings
conference   : Eurographics 2023
pages        :
year         : 2023
month        : May
title        : Physics-Informed Neural Corrector for Deformation-based Fluid Control
subtitle     :
author       : Jingwei Tang, Byungsoo Kim, Vinicius C. Azevedo, Barbara Solenthaler
booktitle    : The Eurographics Association and John Wiley & Sons Ltd.
ISSN/ISBN    :
editor       : 
publisher    :
publ.place   :
volume       : 2
issue        : 42
language     : English
keywords     : 
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 them leadingthat they lead to physical and eventually visually appealing modifications on edited fluid data.