Derivative-based Sensitivity Analysis

Derivative-Based Sensitivity Analysis is a sensitivity analysis method that quantifies the influence of input variables on a model's output by evaluating the partial derivatives of the output function with respect to each input, thereby distinguishing itself from variance- or sampling-based approaches through its reliance on local gradient information.

139

Publications

11.7K

Citations

283

Authors

151

Institutions

Publications per year

2017–2026

23

Authors

283

  1. SK
    Sergei Kucherenko

    Imperial College London, United Kingdom

    8 publications · 1.1K citations

    8

    h-index

  2. DG
    Dan Gabriel Cacuci

    University of South Carolina, United States

    5 publications · 220 citations

    5

    h-index

  3. RT
    Raphael T. Haftka

    University of Florida, United States

    5 publications · 741 citations

    5

    h-index

  4. JS
    Jaroslaw Sobieszczanski‐Sobieski

    Langley Research Center, United States

    5 publications · 347 citations

    5

    h-index

  5. ML
    Matieyendou Lamboni

    University of French Guiana, French Guiana

    4 publications · 206 citations

    4

    h-index

1–5 of 283

Venues

  1. AIAA Journal

    12 publications · 1.1K citations

    12

    h-index

  2. Reliability Engineering & System Safety

    6 publications · 526 citations

    6

    h-index

  3. Mathematics and Computers in Simulation

    5 publications · 631 citations

    5

    h-index

  4. Computers & Chemical Engineering

    4 publications · 227 citations

    4

    h-index

  5. Nuclear Science and Engineering

    4 publications · 241 citations

    4

    h-index

1–5