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  2. Regning og matematisk analyse
  3. Differentialregning & ligninger

Deep Learning for Fluid Simulation and Animation

- Fundamentals, Modeling, and Case Studies
Af: Gilson Antonio Giraldi, Leandro Tavares da Silva, Antonio Lopes Apolinario Jr., Liliane Rodrigues de Almeida Engelsk Paperback

Deep Learning for Fluid Simulation and Animation

- Fundamentals, Modeling, and Case Studies
Af: Gilson Antonio Giraldi, Leandro Tavares da Silva, Antonio Lopes Apolinario Jr., Liliane Rodrigues de Almeida Engelsk Paperback
Tjek vores konkurrenters priser
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.
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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.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 164
ISBN-13: 9783031423321
Indbinding: Paperback
Udgave:
ISBN-10: 3031423321
Kategori: Differentialregning & ligninger
Udg. Dato: 25 nov 2023
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer International Publishing AG
Oplagsdato: 25 nov 2023
Forfatter(e): Gilson Antonio Giraldi, Leandro Tavares da Silva, Antonio Lopes Apolinario Jr., Liliane Rodrigues de Almeida
Forfatter(e) Gilson Antonio Giraldi, Leandro Tavares da Silva, Antonio Lopes Apolinario Jr., Liliane Rodrigues de Almeida

Kategori Differentialregning & ligninger

ISBN-13 9783031423321

Sprog Engelsk

Indbinding Paperback

Sider 164

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 25 nov 2023

Oplagsdato 25 nov 2023

Forlag Springer International Publishing AG

Kategori sammenhænge