Volume decomposition for a robust, mechanics-based, hyperelastic foam model

2015 ◽  
pp. 697-703
Author(s):  
M Lewis ◽  
P Rangaswamy
Keyword(s):  
Foam Model ◽  
Volume Decomposition

scholarly journals MEASUREMENTS AND INFORMATION IN SPIN FOAM MODELS

2012 ◽  
Vol 27 (28) ◽  
pp. 1250164
Author(s):  
J. MANUEL GARCÍA-ISLAS
Keyword(s):  
Information Theory ◽  
Quantum Gravity ◽  
Cosmological Constant ◽  
Shannon Entropy ◽  
Three Dimensional ◽  
Spin Network ◽  
Spin Foam Models ◽  
Foam Model ◽  
Spin Foam Model ◽  
Spin Foam

In the three-dimensional spin foam model of quantum gravity with a cosmological constant, there exists a set of observables associated with spin network graphs. A set of probabilities is calculated from these observables, and hence the associated Shannon entropy can be defined. We present the Shannon entropy associated with these observables and find some interesting bounded inequalities. The problem relates measurements, entropy and information theory in a simple way which we explain.

scholarly journals A spin foam model without bubble divergences

2001 ◽  
Vol 599 (1-2) ◽  
pp. 255-282 ◽  
Author(s):  
Alejandro Perez ◽  
Carlo Rovelli
Keyword(s):  
Foam Model ◽  
Spin Foam Model ◽  
Spin Foam

scholarly journals Development of a Crushable Foam Model for Human Trabecular Bone

2021 ◽  
Author(s):  
Navid Soltanihafshejani ◽  
Thom Bitter ◽  
Dennis Janssen ◽  
Nico Verdonschot
Keyword(s):  
Trabecular Bone ◽  
Human Trabecular Bone ◽  
Foam Model

scholarly journals Volume Decomposition for Hierarchical Skeletonization

2009 ◽  
Vol 8 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Xiaopeng Zhang ◽  
Jianfei Liu ◽  
Marc Jaeger ◽  
Zili Li
Keyword(s):  
Initial Data ◽  
Hierarchical Structure ◽  
Shape Description ◽  
Shape Features ◽  
Volume Data ◽  
Volume Decomposition ◽  
The Hierarchical Structure

Hierarchical skeletons and shape components are important shape features, and they are useful for shape description and shape understanding. Techniques to extract shape components and hierarchical skeletons from volume data are analyzed in this paper based on multiple distance transformations. The application of volume decomposition for the extraction of hierarchical skeletons is emphasized and specified here. This work includes an establishment of the hierarchical structure of the object volume, a decomposition of the volume into simple sub-volumes, an extraction of compact skeleton segments corresponding to each independent sub-volume, and a connection of these skeleton segments into a hierarchical structure reflecting the organization the initial data

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