03711nam 22006375 450 991076689370332120231124120448.03-031-42333-X10.1007/978-3-031-42333-8(MiAaPQ)EBC30970277(Au-PeEL)EBL30970277(DE-He213)978-3-031-42333-8(EXLCZ)992903859560004120231124d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Learning for Fluid Simulation and Animation[electronic resource] Fundamentals, Modeling, and Case Studies /by Gilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinário Jr., Leandro Tavares da Silva1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (172 pages)SpringerBriefs in Mathematics,2191-8201Print version: Giraldi, Gilson Antonio Deep Learning for Fluid Simulation and Animation Cham : Springer International Publishing AG,c2023 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.SpringerBriefs in Mathematics,2191-8201Differential equationsArtificial intelligenceSoft condensed matterComputer simulationDifferential EquationsArtificial IntelligenceFluidsComputer ModellingDifferential equations.Artificial intelligence.Soft condensed matter.Computer simulation.Differential Equations.Artificial Intelligence.Fluids.Computer Modelling.515.35Giraldi Gilson Antonio1449935Almeida Liliane Rodrigues de1449936Apolinário Jr Antonio Lopes1449937Silva Leandro Tavares da1449938MiAaPQMiAaPQMiAaPQBOOK9910766893703321Deep Learning for Fluid Simulation and Animation3648673UNINA