Data-driven Equation Of State Modeling

Data-Driven Equation of State Modeling is a computational modeling approach within thermodynamic theory that constructs equations of state directly from empirical or simulated data using statistical and machine learning techniques, distinguishing it from traditional methods reliant on analytical physical assumptions.

51

Publications

3.1K

Citations

185

Authors

103

Institutions

Publications per year

2017–2026

33

Authors

185

  1. JV
    Jadran Vrabec

    Paderborn University, Germany

    2 publications · 66 citations

    2

    h-index

  2. RS
    Roland Span

    Ruhr University Bochum, Germany

    2 publications · 66 citations

    2

    h-index

  3. GR
    Gábor Rutkai

    Paderborn University, Germany

    2 publications · 66 citations

    2

    h-index

  4. SL
    Steven L. Brunton

    University of Washington, United States

    2 publications · 96 citations

    2

    h-index

  5. MT
    Monika Thol

    Ruhr University Bochum, Germany

    2 publications · 66 citations

    2

    h-index

1–5 of 185

Venues

  1. Fluid Phase Equilibria

    6 publications · 223 citations

    6

    h-index

  2. Applied Energy

    3 publications · 215 citations

    3

    h-index

  3. Chemical Engineering Science

    3 publications · 69 citations

    3

    h-index

  4. The Journal of Chemical Physics

    2 publications · 57 citations

    2

    h-index

  5. Theoretical and Computational Fluid Dynamics

    2 publications · 119 citations

    2

    h-index

1–5