Differential Methods for Multi-Dimensional Visual Data Analysis

  • Werner Benger
  • René Heinzl
  • Dietmar Hildenbrand
  • Tino Weinkauf
  • Holger Theisel
  • David Tschumperlé
Reference work entry


Images in scientific visualization are the end-product of data processing. Starting from higher-dimensional datasets, such as scalar-, vector-, tensor- fields given on 2D, 3D, 4D domains, the objective is to reduce this complexity to two-dimensional images comprehensible to the human visual system. Various mathematical fields such as in particular differential geometry, topology (theory of discretized manifolds), differential topology, linear algebra, Geometric Algebra, vectorfield and tensor analysis, and partial differential equations contribute to the data filtering and transformation algorithms used in scientific visualization. The application of differential methods is core to all these fields. The following chapter will provide examples from current research on the application of these mathematical domains to scientific visualization and ultimately generating of images for analysis of multi-dimensional datasets.


Vector Field Fiber Bundle Base Space Tensor Field Geometric Algebra 
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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Werner Benger
    • 1
  • René Heinzl
    • 2
  • Dietmar Hildenbrand
    • 3
  • Tino Weinkauf
    • 4
  • Holger Theisel
    • 5
  • David Tschumperlé
    • 6
  1. 1.Center for Computation and Technology at Lousiana State UniversityBaton RougeUSA
  2. 2.Shenteq s.r.oBratislavaSlovak Republic
  3. 3.University of Technology DarmstadtDarmstadtGermany
  4. 4.New York UniversityNew YorkUSA
  5. 5.Institut fur Simulation und Graphik AG Visual ComputingMagdeburgGermany
  6. 6.GREYC (UMR-CNRS 6072)CAEN CedexFrance

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