Physics-Informed Neural Corrector for Deformation-based Fluid Control
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
Description
CCS Concepts: Computing methodologies -> Physical Simulation; Neural networks
@article{10.1111:cgf.14751,
journal = {Computer Graphics Forum},
title = {{Physics-Informed Neural Corrector for Deformation-based Fluid Control}},
author = {Tang, Jingwei and Kim, Byungsoo and Azevedo, Vinicius C. and Solenthaler, Barbara},
year = {2023},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14751}
}