Overview
- Discloses the use of machine learning in fluid simulation as an option of lower computational cost
- Offers a comparison between two neural network approaches and corresponding models
- Intended for students and researchers who need to keep pace with this rapidly developing field
Part of the book series: SpringerBriefs in Mathematics (BRIEFSMATH)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the authors
Antonio Lopes Apolinário Junior is an Associate Professor at the Federal University of Bahia (UFBA), Brazil. He holds a PhD in Systems and Computer Engineering (2004) from the Federal University of Rio de Janeiro, Brazil. His research interests lie in computer graphics, 3D modeling, augmented reality, virtual reality, and physically-based rendering and animation.
Leandro Tavares da Silva is a Professor at the Federal Center for Technological Education “Celso Suckow da Fonseca” (CEFET-RJ), Brazil. He holds a PhD in Computational Modeling (2016) from the National Laboratory for Scientific Computing (LNCC), Brazil. He currently does research on fluid simulation and animation, and deep learning.
Liliane Rodrigues de Almeida is a Fellow Researcher at the National Laboratory for Scientific Computing (LNCC). She holds a Master’s degree in Computer Science (2017) from the Federal University of Juiz de Fora (UFJF), Brazil, and has a degree in Computer Science from the same university. Her fields of research are physical simulation and computational geometry.
Bibliographic Information
Book Title: Deep Learning for Fluid Simulation and Animation
Book Subtitle: Fundamentals, Modeling, and Case Studies
Authors: Gilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinário Jr., Leandro Tavares da Silva
Series Title: SpringerBriefs in Mathematics
DOI: https://doi.org/10.1007/978-3-031-42333-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Softcover ISBN: 978-3-031-42332-1Published: 25 November 2023
eBook ISBN: 978-3-031-42333-8Published: 24 November 2023
Series ISSN: 2191-8198
Series E-ISSN: 2191-8201
Edition Number: 1
Number of Pages: XII, 164
Number of Illustrations: 14 b/w illustrations, 39 illustrations in colour
Topics: Analysis, Artificial Intelligence, Materials Science, general, Simulation and Modeling