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Visualization-based analysis of multiparameter models using environment for N-dimensional model analysis

S Bosan1, T R Harris

  • 1Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA.

Computers and Biomedical Research, an International Journal
|June 1, 1997
PubMed
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This study introduces a new N-dimensional visualization method for analyzing multiparameter models, enhancing sensitivity analysis and adding visual regression for parameter identification. The EN-DIMAN software facilitates this advanced model analysis.

Area of Science:

  • Computational modeling
  • Multiphysics simulation
  • Scientific visualization

Background:

  • Analyzing complex multiparameter models is crucial for scientific research.
  • Existing methods for model analysis can be limited in scope and visualization capabilities.
  • Pulmonary capillary-transport models require robust analysis techniques.

Purpose of the Study:

  • To present an enhanced N-dimensional computer visualization methodology for multiparameter model analysis.
  • To introduce a novel sensitivity analysis calculation and a visual regression step.
  • To apply the methodology to pulmonary capillary-transport models and implement it as user-friendly software.

Main Methods:

  • N-dimensional computer visualization for behavior, sensitivity, and identifiability analysis.

Related Experiment Videos

  • New sensitivity analysis based on the coefficient of variation.
  • Addition of a visual regression step for parameter identification.
  • Application to pulmonary capillary-transport models.

    • Main Results:

    • The enhanced methodology provides a comprehensive view of model analysis, integrating multiple steps.
    • Visual regression effectively visualizes the parameter identification process.
    • The methodology was successfully applied to pulmonary capillary-transport models.
    • A stand-alone software, EN-DIMAN, was developed for GUI-based model analysis.

    Conclusions:

    • The developed N-dimensional visualization methodology offers a powerful approach for multiparameter model analysis.
    • The inclusion of visual regression enhances the understanding of parameter identification.
    • EN-DIMAN software provides an accessible tool for researchers to perform complex model analysis.