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Authors: Wonjung Park ; Hyunsoo Kim and Jinah Park

Affiliation: Korea Advanced Institute of Science and Technology, Daejeon, Korea

Keyword(s): Physical Simulation, Machine Learning, Viscous Fluid, Convolutional Neural Network.

Abstract: We propose a data-driven viscosity solver based on U-shaped convolutional neural network to predict velocity changes due to viscosity. Our solver takes velocity derivatives, fluid volume, and solid indicator quantities as input. The traditional marker-and-cell (MAC) grid stores velocities at the edges of the grid, causing the dimensions of the velocity field vary from axis to axis. In our work, we suggest a symmetric MAC grid that maintains consistent dimensions across axes without interpolation or symmetry breaking. The proposed grid effectively transfers spatial fluid quantities such as partial derivatives of velocity, enabling networks to generate accurate predictions. Additionally, we introduce a physics-based loss inspired by the variational formulation of viscosity to enhance the network’s generalization for a wide range of viscosity coefficients. We demonstrate various fluid simulation results, including 2D and 3D fluid-rigid body scenes and a scene exhibiting the buckling eff ect. (More)

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Paper citation in several formats:
Park, W.; Kim, H. and Park, J. (2024). Data-Driven Viscosity Solver for Fluid Simulation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 269-276. DOI: 10.5220/0012397300003660

@conference{grapp24,
author={Wonjung Park. and Hyunsoo Kim. and Jinah Park.},
title={Data-Driven Viscosity Solver for Fluid Simulation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP},
year={2024},
pages={269-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012397300003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP
TI - Data-Driven Viscosity Solver for Fluid Simulation
SN - 978-989-758-679-8
IS - 2184-4321
AU - Park, W.
AU - Kim, H.
AU - Park, J.
PY - 2024
SP - 269
EP - 276
DO - 10.5220/0012397300003660
PB - SciTePress

- Science and Technology Publications, Lda.
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