Uncertainty Propagation Visualization

Uncertainty Propagation Visualization is a visualization approach that specifically represents how uncertainties in input data or model parameters evolve and influence outputs across computational or inferential processes, distinguishing itself from related uncertainty visualizations by emphasizing the dynamic transmission and transformation of uncertainty through chained or layered analytical steps.

6

Publications

818

Citations

17

Authors

7

Institutions

Publications per year

2017–2026

2

Authors

17

  1. CM
    Craig M. Wittenbrink

    University of California, Santa Cruz, United States

    2 publications · 609 citations

    2

    h-index

  2. AP
    Alex Pang

    University of California, Santa Cruz, United States

    2 publications · 609 citations

    2

    h-index

  3. SK
    Suresh K. Lodha

    University of California, Santa Cruz, United States

    2 publications · 609 citations

    2

    h-index

  4. GY
    Guo-Xun Yuan

    National Taiwan University, Taiwan

    1 publications · 49 citations

    1

    h-index

  5. RS
    R. Sheehan

    Boston College, United States

    1 publications · 67 citations

    1

    h-index

1–5 of 17

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 49 citations

    1

    h-index

  2. The Visual Computer

    1 publications · 542 citations

    1

    h-index

  3. Environmental Modelling & Software

    1 publications · 55 citations

    1

    h-index

  4. Journal of Computer Languages

    1 publications · 16 citations

    1

    h-index

  5. it - Information Technology

    1 publications · 15 citations

1–5