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 br...
Saved in:
| Main Authors: | , , , |
|---|---|
| Institution som forfatter: | |
| Format: | Electronisk eBog |
| Sprog: | engelsk |
| Udgivet: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
|
| Udgivelse: | 1st ed. 2023. |
| Serier: | SpringerBriefs in Mathematics,
|
| Fag: | |
| Online adgang: | https://doi.org/10.1007/978-3-031-42333-8 |
| Tags: |
Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-42333-8 | ||
| 003 | DE-He213 | ||
| 005 | 20260906075651.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 231124s2023 sz | o |||| 0|eng d | ||
| 020 | |a 9783031423338 |9 978-3-031-42333-8 | ||
| 024 | 7 | |a 10.1007/978-3-031-42333-8 |2 doi | |
| 050 | 4 | |a QA370-380 | |
| 072 | 7 | |a PBKJ |2 bicssc | |
| 072 | 7 | |a MAT007000 |2 bisacsh | |
| 072 | 7 | |a PBKJ |2 thema | |
| 082 | 0 | 4 | |a 515.35 |2 23 |
| 100 | 1 | |a Giraldi, Gilson Antonio. |e author. |0 (orcid)0000-0003-0623-9461 |1 https://orcid.org/0000-0003-0623-9461 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Deep Learning for Fluid Simulation and Animation |h [electronic resource] : |b Fundamentals, Modeling, and Case Studies / |c by Gilson Antonio Giraldi, Liliane Rodrigues de Almeida, Antonio Lopes Apolinário Jr., Leandro Tavares da Silva. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XII, 164 p. 53 illus.). |b 39 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of colour is Not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background colour |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All Non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does Not fully comply with PDF/UA standards but does include limited screen reader support, descriptions for Non-text content (such as images and graphs), bookmarks for easy navigation, and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognise the importance of accessibility and welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us via https://www.springernature.com/gp/info/accessibility | |
| 532 | 8 | |a Inaccessible, or known limited accessibility | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Differential equations. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Soft condensed matter. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 1 | 4 | |a Differential Equations. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Fluids. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 700 | 1 | |a Almeida, Liliane Rodrigues de. |e author. |0 (orcid)0000-0002-8933-4781 |1 https://orcid.org/0000-0002-8933-4781 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Apolinário Jr., Antonio Lopes. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Silva, Leandro Tavares da. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031423321 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031423345 |
| 830 | 0 | |a SpringerBriefs in Mathematics, |x 2191-8201 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-42333-8 |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||