Fast Cloth Simulation with Parallel Computers

2000 ◽  
pp. 491-499 ◽  
Author(s):  
Sergio Romero ◽  
Luis F. Romero ◽  
Emilio L. Zapata
Keyword(s):  
Parallel Computers ◽  
Cloth Simulation

Real-time cloth simulation based on particle constraints

2011 ◽  
Vol 31 (1) ◽  
pp. 289-292
Author(s):  
Zhong-hua LU ◽  
Ding-fang CHEN
Keyword(s):  
Real Time ◽  
Cloth Simulation ◽  
Simulation Based

Optimization of Geometric Structures of New Materials on Parallel Computers

1998 ◽  
Author(s):  
John H. Weare ◽  
Ryoichi Kawai ◽  
Beth Ong
Keyword(s):  
Parallel Computers ◽  
New Materials ◽  
Geometric Structures

Microstructures in Elastic Media

1994 ◽  
Author(s):  
Nhan Phan-Thien ◽  
Sangtae Kim
Keyword(s):  
Computational Methods ◽  
High Performance ◽  
Chemical Engineering ◽  
Elliptic Partial Differential Equations ◽  
Parallel Computers ◽  
Computational Algorithms ◽  
Applied Physics ◽  
Advanced Students ◽  
Particulate Solids ◽  
Modeling Software

This monograph describes various methods for solving deformation problems of particulate solids, taking the reader from analytical to computational methods. The book is the first to present the topic of linear elasticity in mathematical terms that will be familiar to anyone with a grounding in fluid mechanics. It incorporates the latest advances in computational algorithms for elliptic partial differential equations, and provides the groundwork for simulations on high performance parallel computers. Numerous exercises complement the theoretical discussions, and a related set of self-documented programs is available to readers with Internet access. The work will be of interest to advanced students and practicing researchers in mechanical engineering, chemical engineering, applied physics, computational methods, and developers of numerical modeling software.

scholarly journals Practical Wavelet Tree Construction

2021 ◽  
Vol 26 ◽  
pp. 1-67
Author(s):  
Patrick Dinklage ◽  
Jonas Ellert ◽  
Johannes Fischer ◽  
Florian Kurpicz ◽  
Marvin Löbel
Keyword(s):  
Parallel Algorithms ◽  
Shared Memory ◽  
Distributed Memory ◽  
Auxiliary Information ◽  
Parallel Computers ◽  
External Memory ◽  
Sequential Algorithms ◽  
Bottom Up ◽  
Memory Efficiency ◽  
Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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