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Author Giraldi, Gilson Antonio, author

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
Published Cham : Springer, 2023


Description 1 online resource (xii, 164 pages) : illustrations (some color)
Series SpringerBriefs in mathematics, 2191-8201
SBMAC SpringerBriefs
SpringerBriefs in mathematics, 2191-8201
Contents 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
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 Includes bibliographical references and index
Notes Online resource; title from PDF title page (SpringerLink, viewed December 4, 2023)
Subject Deep learning (Machine learning)
Fluid mechanics -- Computer simulation
Deep learning (Machine learning)
Fluid mechanics -- Computer simulation.
Form Electronic book
Author Almeida, Liliane Rodrigues de, author
Apolinário, Antonio Lopes, author
Silva, Leandro Tavares da, author
ISBN 9783031423338