Automatic Differentiation

Automatic Differentiation is a computational method that systematically evaluates the exact derivatives of a function by applying the chain rule to elementary arithmetic operations in a computational graph, distinguishing it from symbolic or numerical differentiation through its decomposition of complex functions into analytically differentiable primitives.

838

Publications

70K

Citations

2.2K

Authors

782

Institutions

Publications per year

2017–2026

407

Authors

2.2K

  1. CB
    Christian Bischof

    Argonne National Laboratory, United States

    35 publications · 2.1K citations

    23

    h-index

  2. AG
    Andreas Griewank

    Humboldt-Universität zu Berlin, Germany

    19 publications · 2K citations

    16

    h-index

  3. HM
    H. Martin Bücker

    RWTH Aachen University, Germany

    12 publications · 385 citations

    11

    h-index

  4. PH
    Paul Hovland

    Argonne National Laboratory, United States

    10 publications · 744 citations

    10

    h-index

  5. LH
    Laurent Hascoët

    Institut national de recherche en sciences et technologies du numérique, France

    9 publications · 896 citations

    9

    h-index

1–5 of 2.2K

Venues

  1. arXiv (Cornell University)

    Repository · 88 publications · 5.6K citations

    32

    h-index

  2. ACM Transactions on Mathematical Software

    TOMS · 29 publications · 3.3K citations

    22

    h-index

  3. Optimization methods & software

    22 publications · 2.9K citations

    18

    h-index

  4. SIAM Journal on Scientific Computing

    16 publications · 1.3K citations

    14

    h-index

  5. ACM Transactions on Graphics

    TOG · 15 publications · 742 citations

    13

    h-index

1–5