Deep Learning for Fluid Simulation and Animation

This book PDF is perfect for those who love Artificial intelligence genre, written by Gilson Antonio Giraldi and published by Springer Nature which was released on 08 May 2024 with total hardcover pages 173. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Deep Learning for Fluid Simulation and Animation books below.

Deep Learning for Fluid Simulation and Animation
Deep Learning for Fluid Simulation and Animation
Author : Gilson Antonio Giraldi
File Size : 54,8 Mb
Publisher : Springer Nature
Language : English
Release Date : 08 May 2024
ISBN : 9783031423338
Pages : 173 pages
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Deep Learning for Fluid Simulation and Animation by Gilson Antonio Giraldi Book PDF Summary

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.

Deep Learning for Fluid Simulation and Animation

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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,

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