Fr. 179.00

Topological Methods in Data Analysis and Visualization II - Theory, Algorithms, and Applications

English · Hardback

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Description

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When scientists analyze datasets in a search for underlying phenomena, patterns or causal factors, their first step is often an automatic or semi-automatic search for structures in the data. Of these feature-extraction methods, topological ones stand out due to their solid mathematical foundation. Topologically defined structures-as found in scalar, vector and tensor fields-have proven their merit in a wide range of scientific domains, and scientists have found them to be revealing in subjects such as physics, engineering, and medicine.

Full of state-of-the-art research and contemporary hot topics in the subject, this volume is a selection of peer-reviewed papers originally presented at the fourth Workshop on Topology-Based Methods in Data Analysis and Visualization, TopoInVis 2011, held in Zurich, Switzerland. The workshop brought together many of the leading lights in the field for a mixture of formal presentations and discussion. One topic currently generating a great deal of interest, and explored in several chapters here, is the search for topological structures in time-dependent flows, and their relationship with Lagrangian coherent structures. Contributors also focus on discrete topologies of scalar and vector fields, and on persistence-based simplification, among other issues of note. The new research results included in this volume relate to all three key areas in data analysis-theory, algorithms and applications.

List of contents

Part I: Discrete Morse Theory.- Part II: Hierarchical Methods for Extracting and Visualizing Topological Structures.- Part III: Visualization of Dynamical Systems, Vector and Tensor Fields.- Part IV: Topological Visualization of Unsteady Flow.

Summary

When scientists analyze datasets in a search for underlying phenomena, patterns or causal factors, their first step is often an automatic or semi-automatic search for structures in the data. Of these feature-extraction methods, topological ones stand out due to their solid mathematical foundation. Topologically defined structures—as found in scalar, vector and tensor fields—have proven their merit in a wide range of scientific domains, and scientists have found them to be revealing in subjects such as physics, engineering, and medicine. Full of state-of-the-art research and contemporary hot topics in the subject, this volume is a selection of peer-reviewed papers originally presented at the fourth Workshop on Topology-Based Methods in Data Analysis and Visualization, TopoInVis 2011, held in Zurich, Switzerland. The workshop brought together many of the leading lights in the field for a mixture of formal presentations and discussion. One topic currently generating a great deal of interest, and explored in several chapters here, is the search for topological structures in time-dependent flows, and their relationship with Lagrangian coherent structures. Contributors also focus on discrete topologies of scalar and vector fields, and on persistence-based simplification, among other issues of note. The new research results included in this volume relate to all three key areas in data analysis—theory, algorithms and applications.

Product details

Assisted by Hamish Carr (Editor), Hamish Carr et al (Editor), Raphael Fuchs (Editor), Helwi Hauser (Editor), Helwig Hauser (Editor), Ronald Peikert (Editor)
Publisher Springer, Berlin
 
Languages English
Product format Hardback
Released 01.02.2012
 
EAN 9783642231742
ISBN 978-3-642-23174-2
No. of pages 299
Dimensions 156 mm x 245 mm x 18 mm
Weight 652 g
Illustrations XI, 299 p.
Series Mathematics and Visualization
Mathematics and Visualization
Subjects Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics

B, Algorithms, Artificial Intelligence, Mathematics, Visualization, Mathematics and Statistics, Computer Graphics, Numerical analysis, Data and Information Visualization, Graphics programming, Data Analysis;Geometry;Topology;Visualization

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