Deep learning for fluid simulation and animation : fundamentals, modeling, and case studies / Gilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinário Jr., Leandro Tavares da Silva.
Giraldi, Gilson Antonio, author.; Almeida, Liliane Rodrigues de, author.; Apolinário, Antonio Lopes, author.; Silva, Leandro Tavares da, author.
2023
Q325.73
Available Online

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Title Deep learning for fluid simulation and animation : fundamentals, modeling, and case studies / Gilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinário Jr., Leandro Tavares da Silva.
Author Giraldi, Gilson Antonio, author.
ISBN 9783031423338 (electronic bk.)
303142333X (electronic bk.)
9783031423321
3031423321
DOI https://doi.org/10.1007/978-3-031-42333-8
Published Cham : Springer, 2023.
Language English
Description 1 online resource (xii, 164 pages) : illustrations (some color).
Item Number 10.1007/978-3-031-42333-8 doi
Call Number Q325.73
Dewey Decimal Classification 006.3/1
Summary This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods – and at a lower computational cost. This work starts with a brief review of computability theory, aimed to convince the reader – more specifically, researchers of more traditional areas of mathematical modeling – about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed. The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed December 4, 2023).
Added Author Almeida, Liliane Rodrigues de, author.
Apolinário, Antonio Lopes, author.
Silva, Leandro Tavares da, author.
Series SBMAC SpringerBriefs
SpringerBriefs in mathematics, 2191-8201
Available in Other Form Deep Learning for Fluid Simulation and Animation
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction
Fluids and Deep Learning: A Brief Review
Fluid Modeling through Navier-Stokes Equations and Numerical Methods
Why Use Neural Networks for Fluid Animation
Modeling Fluids through Neural Networks
Fluid Rendering
Traditional Techniques
Advanced Techniques
Deep Learning in Rendering
Case Studies
Perspectives
Discussion and Final Remarks
References.

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