Methods and data for: Modeling hydrogen flames with physics-informed neural networks
Abstract
Research data and scripts used to generate the results presented in Chapter 2 titled Modeling hydrogen flames with physics-informed neural networks of the doctoral dissertation of E.Bunschoten titled Consistent data-driven equations of state for reacting and nonideal compressible fluids. Instructions for the various steps are found in the README file. The data set includes the python scripts used for generating the flamelet solutions and training the neural networks, the SU2 configuration files and flow solutions, and the detailed chemistry solution data used for validation.
Fields of Study & Subjects
Model Reduction and Neural Networks
Combustion and flame dynamics
Computational Fluid Dynamics and Aerodynamics
Publication details
- Journal / source
- 4TU.ResearchData
- Publication date
- 30 January 2026
- Language
- EN
- Document type
- Dataset