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Fluid control using the adjoint method

Authors:

Antoine McNamara,

Adrien Treuille,

Zoran Popović,

Jos StamAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 23, Issue 3

Pages 449 - 456

https://doi.org/10.1145/1015706.1015744

Published: 01 August 2004 Publication History

Abstract

We describe a novel method for controlling physics-based fluid simulations through gradient-based nonlinear optimization. Using a technique known as the adjoint method, derivatives can be computed efficiently, even for large 3D simulations with millions of control parameters. In addition, we introduce the first method for the full control of free-surface liquids. We show how to compute adjoint derivatives through each step of the simulation, including the fast marching algorithm, and describe a new set of control parameters specifically designed for liquids.

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Index Terms

Fluid control using the adjoint method
1. Computing methodologies
  1. Computer graphics
    1. Animation

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 23, Issue 3

August 2004

684 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1015706

Editor:
John C. Hart

Issue’s Table of Contents

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 August 2004

Published in TOG Volume 23, Issue 3

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Sun YCucuzzella EBrus SNarayanan SNadiga BVan Roekel LHückelheim JMadireddy SHeimbach PEvans KSchenk O(2024)Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural SurrogatesProceedings of the Platform for Advanced Scientific Computing Conference10.1145/3659914.3659920(1-10)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3659914.3659920
Chen YLevin DLanglois T(2024)Fluid Control with Laplacian EigenfunctionsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657468(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657468
Teh AGkioulekas IO'Toole M(2024)Aperture-Aware Lens DesignACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657398(1-10)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657398
Xiao STong CZhang QCen YLi FLiang X(2024)Laplacian Projection Based Global Physical Prior Smoke ReconstructionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.335863630:12(7657-7671)Online publication date: Dec-2024
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