Picture: Felix Döppel

Felix Döppel Dr.-Ing.

Working area(s)

Scientific Machine Learning

Contact

ML-MORE: Machine Learning and ModelOrderREduction for the prediction of catalytic filter efficiencies

KIARA: Artificial Intelligence for the identification of the ageing effects of exhaust gas catalysts in real applications

  • 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