Methods and data for: Modeling of Nonideal Compressible Fluids with Physics-Informed Neural Networks
Abstract
Scripts and data used to produce the results presented in E.Bunschoten, A.Cappiello, M.Pini: "Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning" in Computers and Fluids 306,106932, 2026. doi: https://doi.org/10.1016/j.compfluid.2025.106932 and in Chapter 3 of the doctoral dissertation of E.Bunschoten titled "Consistent data-driven equations of state for reacting and nonideal compressible fluids".
Fields of Study & Subjects
Model Reduction and Neural Networks
Phase Equilibria and Thermodynamics
Machine Learning in Materials Science
Publication details
- Journal / source
- 4TU.ResearchData
- Publication date
- 30 January 2026
- Language
- EN
- Document type
- Dataset