Model-to-model Refinement

Model-to-Model Refinement is a model transformation approach that belongs to the broader category of model-to-model transformations, distinguished by its focus on incrementally enhancing the level of detail, precision, or completeness of a source model while preserving its semantic essence to produce a more refined target model.

29

Publications

1.8K

Citations

99

Authors

48

Institutions

Publications per year

2017–2026

5

Authors

99

  1. DD
    Daniel D. Gajski

    University of California, Irvine, United States

    2 publications · 32 citations

    2

    h-index

  2. SB
    Smita Bakshi

    University of California, Irvine, United States

    2 publications · 32 citations

    2

    h-index

  3. JG
    Jie Gong

    Rutgers, The State University of New Jersey, United States

    2 publications · 32 citations

    2

    h-index

  4. WE
    Wouter Edeling

    Centrum Wiskunde & Informatica, Netherlands

    1 publications · 16 citations

    1

    h-index

  5. DK
    Dieter K. Schneider

    Brookhaven National Laboratory, United States

    1 publications · 12 citations

    1

    h-index

1–5 of 99

Venues

  1. 1

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 1 publications · 68 citations

    1

    h-index

  3. IEEE Access

    1 publications · 19 citations

    1

    h-index

  4. Biotechnology Journal

    1 publications · 19 citations

    1

    h-index

  5. SIMULATION

    1 publications · 12 citations

    1

    h-index

1–5