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＜電子ブック＞
Deep Learning for Fluid Simulation and Animation : Fundamentals, Modeling, and Case Studies / by Gilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinário Jr., Leandro Tavares da Silva
(SpringerBriefs in Mathematics. ISSN:21918201)

版	1st ed. 2023.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2023
本文言語	英語
大きさ	XII, 164 p. 53 illus., 39 illus. in color : online resource
著者標目	*Giraldi, Gilson Antonio author Almeida, Liliane Rodrigues de author Apolinário Jr., Antonio Lopes author Silva, Leandro Tavares da author SpringerLink (Online service)
件　名	LCSH:Differential equations LCSH:Artificial intelligence LCSH:Soft condensed matter LCSH:Computer simulation FREE:Differential Equations FREE:Artificial Intelligence FREE:Fluids FREE:Computer Modelling
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-3-031-42333-8

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