Pseudo-random Number Generation

Pseudo-Random Number Generation is a deterministic computational process that produces sequences of numbers approximating the properties of true random sequences, distinguished from true random generation by its reliance on an initial seed value and algorithmic reproducibility.

1.1K

Publications

74.8K

Citations

2.4K

Authors

953

Institutions

Publications per year

2017–2026

322

Authors

2.4K

  1. HN
    Harald Niederreiter

    Austrian Academy of Sciences, Austria

    42 publications · 5.1K citations

    25

    h-index

  2. JE
    Jürgen Eichenauer‐Herrmann

    Darmstadt University of Applied Sciences, Germany

    20 publications · 425 citations

    13

    h-index

  3. AW
    Arne Winterhof

    Austrian Academy of Sciences, Austria

    15 publications · 291 citations

    11

    h-index

  4. XW
    Xingyuan Wang

    Dalian University of Technology, China

    9 publications · 343 citations

    9

    h-index

  5. IE
    Igor E. Shparlinski

    Macquarie University, Australia

    8 publications · 327 citations

    8

    h-index

1–5 of 2.4K

Venues

  1. Nonlinear Dynamics

    19 publications · 1.4K citations

    18

    h-index

  2. Mathematics of Computation

    MCOM · 30 publications · 796 citations

    16

    h-index

  3. IEEE Access

    23 publications · 767 citations

    16

    h-index

  4. Journal of the ACM

    JACM · 17 publications · 1K citations

    16

    h-index

  5. IEEE Transactions on Information Theory

    13 publications · 929 citations

    13

    h-index

1–5