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Deep learning for fluid simulation a...

Giraldi, Gilson Antonio.

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Deep learning for fluid simulation and animation = fundamentals, modeling, and case studies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning for fluid simulation and animation/ by Gilson Antonio Giraldi ... [et al.].
其他題名:	fundamentals, modeling, and case studies /
其他作者:	Giraldi, Gilson Antonio.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xii, 164 p. :illustrations (some col.), digital ;24 cm.
內容註:	Introduction -- Fluids and Deep Learning: A Brief Review -- Fluid Modeling through Navier-Stokes Equations and Numerical Methods -- Why Use Neural Networks for Fluid Animation -- Modeling Fluids through Neural Networks -- Fluid Rendering -- Traditional Techniques -- Advanced Techniques -- Deep Learning in Rendering -- Case Studies -- Perspectives -- Discussion and Final Remarks -- References.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning) -
電子資源:	https://doi.org/10.1007/978-3-031-42333-8
ISBN:	9783031423338

Deep learning for fluid simulation and animation = fundamentals, modeling, and case studies /
Deep learning for fluid simulation and animationfundamentals, modeling, and case studies /[electronic resource] :by Gilson Antonio Giraldi ... [et al.]. - Cham :Springer International Publishing :2023. - xii, 164 p. :illustrations (some col.), digital ;24 cm. - SpringerBriefs in mathematics,2191-8201. - SpringerBriefs in mathematics..

Introduction -- Fluids and Deep Learning: A Brief Review -- Fluid Modeling through Navier-Stokes Equations and Numerical Methods -- Why Use Neural Networks for Fluid Animation -- Modeling Fluids through Neural Networks -- Fluid Rendering -- Traditional Techniques -- Advanced Techniques -- Deep Learning in Rendering -- Case Studies -- Perspectives -- Discussion and Final Remarks -- References.

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.

ISBN: 9783031423338

Standard No.: 10.1007/978-3-031-42333-8doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning for fluid simulation and animation = fundamentals, modeling, and case studies /
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520 $a 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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