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001484494 001__ 1484494
001484494 003__ OCoLC
001484494 005__ 20240117003326.0
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001484494 019__ $$a1410758818$$a1411308130
001484494 020__ $$a9783031423338$$q(electronic bk.)
001484494 020__ $$a303142333X$$q(electronic bk.)
001484494 020__ $$z9783031423321
001484494 020__ $$z3031423321
001484494 0247_ $$a10.1007/978-3-031-42333-8$$2doi
001484494 035__ $$aSP(OCoLC)1411639987
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001484494 050_4 $$aQ325.73
001484494 08204 $$a006.3/1$$223/eng/20231204
001484494 1001_ $$aGiraldi, Gilson Antonio,$$eauthor.
001484494 24510 $$aDeep learning for fluid simulation and animation :$$bfundamentals, modeling, and case studies /$$cGilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinário Jr., Leandro Tavares da Silva.
001484494 264_1 $$aCham :$$bSpringer,$$c2023.
001484494 300__ $$a1 online resource (xii, 164 pages) :$$billustrations (some color).
001484494 336__ $$atext$$btxt$$2rdacontent
001484494 337__ $$acomputer$$bc$$2rdamedia
001484494 338__ $$aonline resource$$bcr$$2rdacarrier
001484494 4901_ $$aSpringerBriefs in mathematics,$$x2191-8201
001484494 4900_ $$aSBMAC SpringerBriefs
001484494 504__ $$aIncludes bibliographical references and index.
001484494 5050_ $$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.
001484494 506__ $$aAccess limited to authorized users.
001484494 520__ $$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.
001484494 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 4, 2023).
001484494 650_6 $$aApprentissage profond.
001484494 650_6 $$aMécanique des fluides$$xSimulation par ordinateur.
001484494 650_0 $$aDeep learning (Machine learning)
001484494 650_0 $$aFluid mechanics$$xComputer simulation.$$0(DLC)sh 85049383
001484494 655_0 $$aElectronic books.
001484494 7001_ $$aAlmeida, Liliane Rodrigues de,$$eauthor.
001484494 7001_ $$aApolinário, Antonio Lopes,$$eauthor.
001484494 7001_ $$aSilva, Leandro Tavares da,$$eauthor.
001484494 77608 $$iPrint version:$$aGiraldi, Gilson Antonio$$tDeep Learning for Fluid Simulation and Animation$$dCham : Springer International Publishing AG,c2023
001484494 830_0 $$aSpringerBriefs in mathematics,$$x2191-8201
001484494 852__ $$bebk
001484494 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-42333-8$$zOnline Access$$91397441.1
001484494 909CO $$ooai:library.usi.edu:1484494$$pGLOBAL_SET
001484494 980__ $$aBIB
001484494 980__ $$aEBOOK
001484494 982__ $$aEbook
001484494 983__ $$aOnline
001484494 994__ $$a92$$bISE