This image showsTom Könecke

Tom Könecke

M.Sc.

Institute of Engineering and Computational Mechanics (ITM)

Contact

+49 711 685 66626
 +49 711 685 66400

Pfaffenwaldring 9
70569 Stuttgart
Deutschland
Room: 3.103

Subject

Uncertainty Quantification and Possibility Theory in Engineering Applications

  1. Behrend, J., Könecke, T., Rosenfelder, M., Schneider, J., Hanss, M., & Eberhard, P. (2026). Possibilistic Uncertainty Modeling with Hyperellipsoids. Proceedings of the 11th International Workshop on Reliable Engineering Computing, 204–218. https://doi.org/10.17877/tudobooks-11.155
  2. Schneider, J., Könecke, T., Ebel, H., & Hanss, M. (2026). Possibilistic Filtering for Reliable Robot Localization. Proceedings of the 11th International Workshop on Reliable Engineering Computing, 193–203. https://doi.org/10.17877/tudobooks-11.157
  3. Schneider, J., Könecke, T., & Hanss, M. (2026). Exploring Imprecise Probabilities in Quantum Algorithms with Possibility Theory. Proceedings in Applied Mathematics and Mechanics (PAMM), 26. https://doi.org/10.1002/pamm.70086
  4. Könecke, T., Schneider, J., & Hanss, M. (2025). Sampling-Based Possibility Theory for Engineering Analysis Under Uncertainty: Inference, Prediction and Optimization. Proceedings of the 35th European Safety and Reliability Conference (ESREL2025) and the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025). https://doi.org/10.3850/978-981-94-3281-3_esrel-sra-e2025-p3044-cd
  5. Könecke, T., Ebel, H., & Hanss, M. (2025). Possibilistic Robot Localization Using Visual Landmarks. Proceedings in Applied Mathematics and Mechanics, 25, Article 1. https://doi.org/10.1002/pamm.70002
  6. Schneider, J., Könecke, T., & Hanss, M. (2025). Possibilistic Neural Networks: Reliable Confidence Predictions under Limited Data. Proceedings of the 6th International Conference on Uncertainty Quantification in Computational Sciences and Engineering - UNCECOMP 2025. https://doi.org/10.7712/120225.12339.21200
  7. Schneider, J., Könecke, T., Ebel, H., & Hanss, M. (2024). Confident Robot Localization by Possibilistic Filtering. Proceedings of the 26th International Congress of Theoretical and Applied Mechanics - ICTAM.
  8. Könecke, T., & Hanss, M. (2024). Exploring possibilistic potentials in uncertainty quantification for modal analysis techniques. Proceedings of USD2024 International Conference on Uncertainty in Structural Dynamics.
  9. Frie, L., Könecke, T., Hanss, M., & Eberhard, P. (2023). Possibilistic Uncertainty Quantification for Parametrically Reduced Models of Dynamic Systems with Many Inputs. In Proceedings of the 49th European Rotorcraft Forum.
  10. Könecke, T., & Hanss, M. (2023). On Processing Heterogeneous Sources of Limited Data for Uncertainty Quantification in a Possibilistic Framework. Proceedings of the 5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2023). https://doi.org/10.7712/120223.10343.19772
  11. Könecke, T., Hose, D., Frie, L., Hanss, M., & Eberhard, P. (2022). Analysis of Mixed Uncertainty through Possibilistic Inference by Using Error Estimation of Reduced Order Surrogate Models. ISMA/USD 2022 Proceedings.
  • July 25, 2022: ITM Statusseminar, Hösbach, "Uncertainty Quantification through Imprecise Probabilities & Possibility Theory".
  • September 13, 2022: 9th International Conference on Uncertainty in Structural Dynamics (USD), Leuven. "Analysis of mixed uncertainty through possibilistic inference by using error estimation of reduced order surrogate models".
  • June 12, 2023: 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP), Athens. "On Processing Heterogeneous Sources of Limited Data for Uncertainty Quantification in a Possibilistic Framework".
  • July 19, 2023: ITM Statusseminar, Hösbach, "Possibility Theory, Baby".
  • March 20, 2024: 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Magdeburg. "Possibilistic Robot Localization Using Visual Landmarks".
  • July 15, 2024: ITM Statusseminar, Monbachtal, "Towards Applied Possibilistic Uncertainty Quantification".
  • September 10, 2024: 10th International Conference on Uncertainty in Structural Dynamics (USD), Leuven. "Exploring Possibilistic Potentials in Uncertainty Quantification for Modal Analysis Techniques".
  • June 17, 2025: 35th European Safety and Reliability Conference (ESREL2025) and the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025), Stavanger. "Sampling-Based Possibility Theory for Engineering Analysis Under Uncertainty: Inference, Prediction and Optimization".
  • July 21, 2025: ITM Statusseminar, Monbachtal, "Tasks, Tools and Challenges of Uncertainty Quantification".
  • July 22, 2026: ITM Statusseminar, Löwenstein, "Non-Probabilistic Collision Avoidance".
  • Propagating Arbitrary Quantum Errors Efficiently Using Possibility Theory, Master Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2025.
    In co-supervision with Jan Schneider, M.Sc.
  • Possibilistic Uncertainty Modeling with Hyperellipsoids, Bachelor Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2025.
    In co-supervision with Mario Rosenfelder, M.Sc.
  • Possibilistic Uncertainty Quantification in Optical Systems Using a Triplet Lens Example, Research Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2025.
    In co-supervision with Mark Kurcsics, M.Sc.
  • Possibilistic Identification of Lumped Masses from Reduced Order Models and Measurement Data, Project Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2025.
    In co-supervision with Lennart Frie, M.Sc.
  • Robust Optimization-based Obstacle Avoidance for Mobile Robots, Bachelor Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Mario Rosenfelder, M.Sc. and Jan Schneider, M.Sc.
  • Numerical Analysis of Modal Parameters of a Guitar Soundboard Under Consideration of Uncertainties, Master Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Pierfrancesco Cillo, M.Sc.
  • Design and Implementation of a µ-Synthesis Controller for an Unstable System in Simulation and Hardware, Project Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Arnim Kargl, M.Sc.
  • Uncertainty Quantification of Quantum Algorithms Using a Possibilistic Approach, Study Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Jan Schneider, M.Sc.
  • Implementation and Optimization of Neural Networks for Possibilistic Uncertainty Representation, Project Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Jan Schneider, M.Sc.
  • Using Possibility Theory to Design a Robust Optimal Control Scheme for a Highly Dynamic System, Master Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Mario Rosenfelder, M.Sc. and Jan Schneider, M.Sc.
  • Possibilistic Uncertainty Modeling in Neural Networks, Bachelor Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Jan Schneider, M.Sc.
  • Identification of Non-measurable Parameters of Mechanical Systems from Experimental Data Using Reduced Order Models, Master Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Lennart Frie, M.Sc.
  • Design and Realization of a Fast Optimal Transform, Seminar Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2024.
    In co-supervision with Lennart Frie, M.Sc.
  • Considering Uncertainty in Optimization-based Control of Mechanical Systems, Bachelor Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2023.
    In co-supervision with Mario Rosenfelder, M.Sc.
  • Dynamic Robot Localization Using a Possibilistic Filter, Master Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2023.
    In co-supervision with Dr.-Ing. Henrik Ebel
  • Development of a Camera-based Localization System for Mobile Robots, Project Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2023.
    In co-supervision with Dr.-Ing. Henrik Ebel
  • Set-Membership Particle Filter for Robot Localization, Bachelor Thesis.
    Institute of Engineering and Computational Mechanics, University of Stuttgart, 2022.
    In co-supervision with Dr.-Ing. Henrik Ebel and Hannes Eschmann, M.Sc.
  • Management of institute vehicles
  • Maintenance and administration of the software package FAMOUS for uncertainty quantification in dynamic systems.
  • Publication management

Submitted publications (not yet accepted)

  1. Könecke, T., Elangasinghe, A., Cillo, P., Ziegler, P., Hanss, M., & Eberhard, P. (2026). Identification of Confidence Distributions for Modal Parameters in the Face of Measurement Uncertainty. Applied Mathematical Modeling. https://doi.org/10.18419/opus-17562
  2. Könecke, T., Kaupp, L., Cillo, P., Ziegler, P., Hanss, M., & Eberhard, P. (2025). Beyond Material Properties: Possibility-Based Uncertainty Propagation in Computational Modal Analysis. https://doi.org/10.18419/opus-17641

Accepted publications (not yet published)

  1. Könecke, T., Schneider, J., & Hanss, M. (2026). Uncertainty Quantification Beyond Probability: Using Possibility Theory for Reliable Modal Analysis. Springer Procceding in Physics.
  2. Elangasinghe, A., Könecke, T., Schneider, J., Rosenfelder, M., Hanss, M., & Eberhard, P. (2025). Possibilistic Filtering and Control of a Highly Dynamic Mechanical System in the Presence of Uncertainty. International Journal of Fuzzy Computation and Modelling.

 Könecke, T. (2022). Untersuchungen zur Nutzbarkeit von Fehlerschätzern für possibilistische Inferenzmethoden in parametrisch reduzierten Ersatzmodellen mechanischer Systeme (Master Thesis MSC–324).

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