EAN: 9783031423321

SPRINGERBRIEFS IN MATHEMATICS DEEP LEARNING F...

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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