Felix Döppel Dr.-Ing.
Working area(s)
Contact
felix.doeppel@polimi.it
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., 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, 96, 6, 759-768] - Kircher, T., Döppel, F. A., Votsmeier, M. (2024). Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data.
[Chemical Engineering Journal, 485, 149863] - Döppel, F. A., & Votsmeier, M. (2024). Robust Mechanism Discovery with Atom Conserving Chemical Reaction Neural Networks.
[Proceedings of the Combustion Institute, 40, 1-4, 105507] - Kircher, T., Döppel, F. A., Votsmeier, M. (2024). Embedding Physics into Neural ODEs to learn Kinetics from Integral Reactors.
[Computer Aided Chemical Engineering, 53, 817-822]
Topic: Efficient implementation of mass transfer controlled kinetics in reactor simulations for industrial ammonia oxidation
Handed in at 14th October 2020