Scientific Visualization

Objectives

The learning objectives of the curricular unit “Scientific Visualisation” aim to promote a broad knowledge of the fundamental principles associated with visualisation, from its historical context to the most current applications. The learning objectives are:

  • Develop skills in data representation,
  • Develop skills in traditional visualisation techniques (including scalar, vector and tensor data)
  • Develop skills in image synthesis algorithms (including rasterisation and ray tracing).
  • Develop skills in volume visualisation,
  • Develop skills in in-situ techniques and advanced topics such as multi-variable visualisation.
  • Develop practical skills with tools such as ParaView, VisIt, VTK, Python and GPU-based visualisation.
  • Develop communication and critical thinking skills
  • Acquires awareness of ethical issues associated with scientific visualization

Program

The course covers various important aspects of visualization, including:

  • evolution, relevance and applications;
  • data representation, traditional techniques such as scalar, vector, and tensor visualization;
  • rendering techniques: rasterization, raytracing, shading, and volume visualization.
  • techniques for in-situ visualization;
  • multi-variate visualization;
  • ethical considerations. The course emphasizes developing practical skills using popular tools like ParaView, VisIt, VTK, Python, and GPU-based visualization.

Bibliography

  • Information Visualization: Perception for Design, Ware, C. Morgan Kaufmann. 2019.
  • Realistic Ray Tracing, Shirley, P., & Morley, K., A K Peters/CRC Press, 2013.
  • Visualization Handbook ,Hansen, C. D., Johnson, C. R., & Goode, A., Academic Press, 2004.
  • Scientific Data Analysis and Visualization ,Ma, K. L., & Huang, J., Academic Press, 2019.

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