國立高雄大學圖資館 |

Language: English

Back

Deep learning for fluid simulation and animationfundamentals, modeling, and case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for fluid simulation and animationby Gilson Antonio Giraldi ... [et al.].
Reminder of title:	fundamentals, modeling, and case studies /
other author:	Giraldi, Gilson Antonio.
Published:	Cham :Springer International Publishing :2023.
Description:	xii, 164 p. :illustrations (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning)
Online resource:	https://doi.org/10.1007/978-3-031-42333-8
ISBN:	9783031423338$q(electronic bk.)

Deep learning for fluid simulation and animationfundamentals, 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$q(electronic bk.)

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

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning for fluid simulation and animationfundamentals, modeling, and case studies /
LDR:02552nmm a2200337 a 4500 001 654066
003 DE-He213
005 20231124120448.0
006 m d
007 cr nn 008maaau
008 240426s2023 sz s 0 eng d
020 $a 9783031423338$q(electronic bk.)
020 $a 9783031423321$q(paper)
024 7 $a 10.1007/978-3-031-42333-8 $2 doi
035 $a 978-3-031-42333-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.73
072 7 $a PBKJ $2 bicssc
072 7 $a MAT034000 $2 bisacsh
072 7 $a PBKJ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.73 $b .D311 2023
245 0 0 $a Deep learning for fluid simulation and animation $h [electronic resource] : $b fundamentals, modeling, and case studies / $c by Gilson Antonio Giraldi ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xii, 164 p. : $b illustrations (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in mathematics, $x 2191-8201
505 0 $a 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.
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.
650 0 $a Deep learning (Machine learning) $3 913129
650 0 $a Fluid mechanics $x Computer simulation. $3 692324
650 1 4 $a Differential Equations. $3 285983
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Fluids. $3 205551
650 2 4 $a Computer Modelling. $3 913151
700 1 $a Giraldi, Gilson Antonio. $3 964823
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in mathematics. $3 558795
856 4 0 $u https://doi.org/10.1007/978-3-031-42333-8
950 $a Mathematics and Statistics (SpringerNature-11649)