Random Matrix Theory

Random Matrix Theory is a mathematical framework within probability theory and statistical physics that studies the eigenvalue and eigenvector distributions of matrices whose entries are random variables, distinguishing it from classical matrix analysis by focusing on universal statistical properties emerging in the limit of large matrix dimensions.

5.9K

Publications

401.1K

Citations

6.2K

Authors

1.9K

Institutions

Publications per year

2017–2026

1.1K

Authors

6.2K

  1. JJ
    J. J. M. Verbaarschot

    Stony Brook University, United States

    48 publications · 5.2K citations

    33

    h-index

  2. PJ
    Peter J. Forrester

    The University of Melbourne, Australia

    67 publications · 3.6K citations

    30

    h-index

  3. ZB
    Zhidong Bai

    Northeast Normal University, China

    47 publications · 5K citations

    29

    h-index

  4. VV
    Van Vu

    University of California, Los Angeles, United States

    39 publications · 4.3K citations

    28

    h-index

  5. YV
    Yan V. Fyodorov

    Petersburg Nuclear Physics Institute, Russia

    47 publications · 3.7K citations

    27

    h-index

1–5 of 6.2K

Venues

  1. Physical Review Letters

    PRL · 321 publications · 32.9K citations

    95

    h-index

  2. Journal of Physics A Mathematical and General

    155 publications · 8K citations

    53

    h-index

  3. Physical Review E

    PRE · 148 publications · 6.8K citations

    47

    h-index

  4. The Annals of Statistics

    92 publications · 11.1K citations

    47

    h-index

  5. The Annals of Probability

    98 publications · 9.4K citations

    47

    h-index

1–5