Felix Döppel M. Sc.
Working area(s)
Contact
felix.doeppel@tu-...
work +49 6151 16-25580
Work
L2|04 E1
Alarich-Weiss-Str.8
64287
Darmstadt
Links
- Döppel, F. A., & Votsmeier, M. (2022). Efficient machine learning based surrogate models for surface kinetics by approximating the rates of the rate-determining steps.
Chemical Engineering Science, 262, 117964.
Click here for the freely available preprint version - Döppel, F. A., & Votsmeier, M. (2023). Efficient Neural Network Models of Chemical Kinetics Using a Latent asinh Rate Transformation.
React. Chem. Eng., 2023,8, 2620-2631 - Döppel, F. A., & Votsmeier, M. (2023). Robust Mechanism Discovery with Atom Conserving Chemical Reaction Neural Networks.
ChemRxiv Preprint - Kircher, T., Döppel, F. A., Votsmeier, M. (2023). A neural network with embedded stoichiometry and thermodynamics for learning kinetics from reactor data.
ChemRxiv Preprint - Kircher, T., Döppel, F. A., Votsmeier, M. (2024). Embedding Physics into Neural ODEs to learn Kinetics from Integral Reactors.
ChemRxiv Preprint - Döppel, F.A., Wenzel, T., Herkert, R., Haasdonk, B., Votsmeier, M. (2024). Goal-Oriented Two-Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations.
Chemie Ingenieur Technik, in production, DOI: 10.1002/cite.202300178
Topic: Efficient implementation of mass transfer controlled kinetics in reactor simulations for industrial ammonia oxidation
Handed in at 14th October 2020