Paperback or Softback. Etat : New. Deep Learning for Fluid Simulation and Animation: Fundamentals, Modeling, and Case Studies 0.57. Book. N� de r�f. du vendeur BBS-9783031423321

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Deep Learning for Fluid Simulation and Animation : Fundamentals, Modeling, and Case Studies

Giraldi, Gilson Antonio; De Almeida, Liliane Rodrigues; Apolin�rio, Antonio Lopes, Jr.; Da Silva, Leandro Tavares

Edit� par Springer, 2023

ISBN 10 : 3031423321 ISBN 13 : 9783031423321

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Deep Learning for Fluid Simulation and Animation: Fundamentals, Modeling, and Case Studies (SpringerBriefs in Mathematics)

Giraldi, Gilson Antonio; Almeida, Liliane Rodrigues de; Apolin�rio Jr., Antonio Lopes; Silva, Leandro Tavares da

Edit� par Springer, 2023

ISBN 10 : 3031423321 ISBN 13 : 9783031423321

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Etat : Hervorragend. Zustand: Hervorragend | Seiten: 180 | Sprache: Englisch | Produktart: B�cher. N� de r�f. du vendeur 42808253/1

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Deep Learning for Fluid Simulation and Animation: Fundamentals, Modeling, and Case Studies (SpringerBriefs in Mathematics)

Giraldi, Gilson Antonio; Almeida, Liliane Rodrigues De; Apolin�rio Jr., Antonio Lopes; Silva, Leandro Tavares Da

Edit� par Springer, 2023

ISBN 10 : 3031423321 ISBN 13 : 9783031423321

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Deep Learning for Fluid Simulation and Animation

Gilson Antonio Giraldi

Edit� par Berlin Springer International Publishing Springer Nov 2023, 2023

ISBN 10 : 3031423321 ISBN 13 : 9783031423321

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 164 pp. Englisch. N� de r�f. du vendeur 9783031423321

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Deep Learning for Fluid Simulation and Animation : Fundamentals, Modeling, and Case Studies

Giraldi, Gilson Antonio; De Almeida, Liliane Rodrigues; Apolinário, Antonio Lopes, Jr.; Da Silva, Leandro Tavares

Edit� par Springer, 2023

ISBN 10 : 3031423321 ISBN 13 : 9783031423321

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Deep Learning for Fluid Simulation and Animation : Fundamentals, Modeling, and Case Studies

Giraldi, Gilson Antonio; De Almeida, Liliane Rodrigues; Apolinário, Antonio Lopes, Jr.; Da Silva, Leandro Tavares

Edit� par Springer, 2023

ISBN 10 : 3031423321 ISBN 13 : 9783031423321

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Deep Learning for Fluid Simulation and Animation : Fundamentals, Modeling, and Case Studies

Gilson Antonio Giraldi

Edit� par Springer International Publishing, 2023

ISBN 10 : 3031423321 ISBN 13 : 9783031423321

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de r�f. du vendeur 9783031423321

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