Data Uncertainty Propagation

Data Uncertainty Propagation is a computational framework that characterizes how uncertainties in input data propagate through a system or model to affect the reliability and precision of its outputs, distinguishing it from other uncertainty analysis methods by explicitly tracing the influence of data-level noise, incompleteness, or variability across processing stages.

102

Publications

6.3K

Citations

367

Authors

185

Institutions

Publications per year

2017–2026

31

Authors

367

  1. DR
    D. Rochman

    Paul Scherrer Institute, Switzerland

    12 publications · 373 citations

    11

    h-index

  2. AJ
    A. J. Koning

    Nuclear Research and Consultancy Group, Netherlands

    8 publications · 284 citations

    8

    h-index

  3. SV
    S.C. van der Marck

    Nuclear Research and Consultancy Group, Netherlands

    5 publications · 222 citations

    5

    h-index

  4. AV
    A. Vasiliev

    Paul Scherrer Institute, Switzerland

    4 publications · 145 citations

    4

    h-index

  5. HF
    H. Ferroukhi

    Paul Scherrer Institute, Switzerland

    4 publications · 145 citations

    4

    h-index

1–5 of 367

Venues

  1. Annals of Nuclear Energy

    5 publications · 259 citations

    5

    h-index

  2. Nuclear Science and Engineering

    4 publications · 132 citations

    4

    h-index

  3. Nuclear Engineering and Technology

    3 publications · 96 citations

    3

    h-index

  4. Journal of the Korean Physical Society

    2 publications · 40 citations

    2

    h-index

  5. 2

    h-index

1–5