Methods and data for: Modeling hydrogen flames with physics-informed neural networks

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Authors: Evert Cornelis Bunschoten
Cited by: 0

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