Tracked shipping to South Africa with premium packaging for just R199 

Ship to
South Africa
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Deep Learning for Fluid Simulation and Animation: Fundamentals, Modeling, and Case Studies
Type
Physical Book
Author
Publisher
Language
English
Pages
164
Format
Paperback
Dimensions
23.4x15.6x1 cm
Weight
0.26 kg.
ISBN13
9783031423321
Categories

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

Gilson Antonio Giraldi (Author) · Liliane Rodrigues de Almeida (Author) · Antonio Lopes Apolinário Jr (Author) · Springer · Paperback

Deep Learning for Fluid Simulation and Animation: Fundamentals, Modeling, and Case Studies - Giraldi, Gilson Antonio ; Almeida, Liliane Rodrigues de ; Apolinário Jr, Antonio Lopes

Cheaper New Book Imported to South Africa
Delivery: 07 Oct - 20 Oct Shipping: 16 to 20 business days.
R 889
Faster New Book Imported to South Africa
Delivery: 02 Oct - 12 Oct Shipping: 13 to 14 business days.
R 1,033
R 889

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

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

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews