Adaptive Basis Selection

Adaptive Basis Selection is a method within the framework of Polynomial Chaos Expansions that systematically identifies and retains the most influential polynomial basis functions based on sensitivity measures or error indicators to improve approximation accuracy and computational efficiency.

90

Publications

4.5K

Citations

239

Authors

138

Institutions

Publications per year

2017–2026

21

Authors

239

  1. RG
    Roger Ghanem

    University of Southern California, United States

    4 publications · 224 citations

    4

    h-index

  2. RR
    Ronald R. Coifman

    Yale University, United States

    3 publications · 289 citations

    3

    h-index

  3. WH
    Wei Hu

    University of Science and Technology of China, China

    3 publications · 163 citations

    3

    h-index

  4. CY
    Chao Yang

    Lawrence Berkeley National Laboratory, United States

    3 publications · 163 citations

    3

    h-index

  5. LL
    Lin Lin

    Lawrence Berkeley National Laboratory, United States

    3 publications · 163 citations

    3

    h-index

1–5 of 239

Venues

  1. The Journal of Chemical Physics

    9 publications · 356 citations

    9

    h-index

  2. 5

    h-index

  3. Journal of Computational Physics

    5 publications · 292 citations

    5

    h-index

  4. Science

    3 publications · 61 citations

    3

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 38 citations

    2

    h-index

1–5