LEADER 03711nam 22006375 450 001 9910766893703321 005 20231124120448.0 010 $a3-031-42333-X 024 7 $a10.1007/978-3-031-42333-8 035 $a(MiAaPQ)EBC30970277 035 $a(Au-PeEL)EBL30970277 035 $a(DE-He213)978-3-031-42333-8 035 $a(EXLCZ)9929038595600041 100 $a20231124d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning for Fluid Simulation and Animation$b[electronic resource] $eFundamentals, Modeling, and Case Studies /$fby Gilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinįrio Jr., Leandro Tavares da Silva 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (172 pages) 225 1 $aSpringerBriefs in Mathematics,$x2191-8201 311 08$aPrint version: Giraldi, Gilson Antonio Deep Learning for Fluid Simulation and Animation Cham : Springer International Publishing AG,c2023 327 $aIntroduction -- 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. 330 $aThis book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods ? and at a lower computational cost. This work starts with a brief review of computability theory, aimed to convince the reader ? more specifically, researchers of more traditional areas of mathematical modeling ? about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed. The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches. 410 0$aSpringerBriefs in Mathematics,$x2191-8201 606 $aDifferential equations 606 $aArtificial intelligence 606 $aSoft condensed matter 606 $aComputer simulation 606 $aDifferential Equations 606 $aArtificial Intelligence 606 $aFluids 606 $aComputer Modelling 615 0$aDifferential equations. 615 0$aArtificial intelligence. 615 0$aSoft condensed matter. 615 0$aComputer simulation. 615 14$aDifferential Equations. 615 24$aArtificial Intelligence. 615 24$aFluids. 615 24$aComputer Modelling. 676 $a515.35 700 $aGiraldi$b Gilson Antonio$01449935 701 $aAlmeida$b Liliane Rodrigues de$01449936 701 $aApolinįrio Jr$b Antonio Lopes$01449937 701 $aSilva$b Leandro Tavares da$01449938 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910766893703321 996 $aDeep Learning for Fluid Simulation and Animation$93648673 997 $aUNINA