Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Interactive Visualization of Large Higher-Order Tetrahedral Data

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Zhou, Yuan 作者
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出版日期
104 頁數
$ 72.32 價格
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9783639042856 圖書編碼

Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 圖書標籤: 統計  復雜係統  原始/圖形思維   


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發表於2024-10-05

Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 著者簡介


Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 著者簡介


Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Interactive Visualization of Large Higher-Order Tetrahedral Data 在線電子書 圖書描述

Computational simulations frequently generate solutions defined over very large tetrahedral volume meshes containing millions of elements. Furthermore, solutions over these meshes may often be expressed using non-linear basis functions. Certain solution techniques may even produce non-conforming meshes. Such data is difficult to visualize interactively, as it is far too large to fit in memory and many common data reduction techniques cannot be applied to non-conforming meshes. Common linear interpolation method cannot faithfully and accurately evaluate the non-linear solutions. In this book, three different methods are proposed to provide accurate, interactive and efficient visualization. The surface method introduces pixel-exact evaluation of higher-order solution data on the GPU, which demonstrates the importance of per-pixel rendering versus linear interpolation for producing high quality visualizations. The interactive volume method adaptively samples points from non-linear solution, decimates points at run time to fit GPU memory limits, and renders them order-independently. The feature method improves efficiency by sampling over feature partitions via TetGrid.

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