Publications of PN 7

  1. 2022

    1. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2022. doi:
    2. P. Gebhardt, X. Yu, A. Köhn, and M. Sedlmair, “MolecuSense: Using Force-Feedback Gloves for Creating and Interacting  with Ball-and-Stick Molecules in VR.” 2022. [Online]. Available:
  2. 2021

    1. B. Weder, J. Barzen, F. Leymann, and M. Salm, “Automated Quantum Hardware Selection for Quantum Workflows,” Electronics, vol. 10, no. 8, Art. no. 8, Apr. 2021, doi: 10.3390/electronics10080984.
    2. M. Salm, J. Barzen, F. Leymann, B. Weder, and K. Wild, “Automating the Comparison of Quantum Compilers for Quantum Circuits,” in Proceedings of the 15th Symposium and Summer School on Service-Oriented Computing (SummerSOC 2021), 2021, pp. 64–80. doi: 10.1007/978-3-030-87568-8_4.
    3. J. Kühnert, D. Göddeke, and M. Herschel, “Provenance-integrated parameter selection and optimization in numerical simulations,” 2021.
    4. K. Képes, F. Leymann, B. Weder, and K. Wild, “SiDD: The Situation-Aware Distributed Deployment System,” in Service-Oriented Computing  -- ICSOC 2020 Workshops, 2021, pp. 72--76.
    5. J. Kneifl and J. Fehr, “Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics,” PAMM, vol. 20, no. S1, Art. no. S1, Mar. 2021, doi: 10.1002/pamm.202000353.
    6. J. Kneifl, D. Grunert, and J. Fehr, “A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning,” International Journal for Numerical Methods in Engineering, Apr. 2021, doi: 10.1002/nme.6712.
    7. F. Kempter, C. Kleinbach, M. Staudenmeyer, and J. Fehr, “An Active Female Human Body Model for Simulation of Rear-End Impact Scenarios,” 2021. doi: 10.1002/pamm.202000068.
    8. F. Grioui and T. Blascheck, Study of Heart Rate Visualizations on a Virtual Smartwatch. 2021. doi:
    9. J. Fehr, C. Himpe, S. Rave, and J. Saak, “Sustainable Research Software Hand-Over,” Journal of Open Research Software, vol. 9, no. 5, Art. no. 5, 2021, doi: 10.5334/jors.307.
    10. R. Diestelkämper, S. Lee, M. Herschel, and B. Glavic, “To not miss the forest for the trees - A holistic approach for explaining missing answers over nested data,” 2021.
  3. 2020

    1. M. Zimmermann, U. Breitenbücher, K. Képes, F. Leymann, and B. Weder, “Data Flow Dependent Component Placement of Data Processing Cloud Applications,” in 2020 IEEE International Conference on Cloud Engineering (IC2E), Apr. 2020, pp. 83–94. doi: 10.1109/IC2E48712.2020.00016.
    2. X. Yu, K. Angerbauer, P. Mohr, D. Kalkofen, and M. Sedlmair, “Perspective Matters: Design Implications for Motion Guidance in Mixed Reality,” 2020.
    3. K. Wild, U. Breitenbücher, K. Képes, F. Leymann, and B. Weder, “Decentralized Cross-Organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models,” in Proceedings of the 32nd Conference on Advanced Information Systems Engineering (CAiSE 2020), Jun. 2020, vol. 12127, pp. 20--35. doi: 10.1007/978-3-030-49435-3_2.
    4. B. Weder, U. Breitenbücher, K. Képes, F. Leymann, and M. Zimmermann, “Deployable Self-Contained Workflow Models,” in Proceedings of the 8th European Conference on Service-Oriented and Cloud Computing (ESOCC 2020), Mar. 2020, pp. 85--96. doi: 10.1007/978-3-030-44769-4_7.
    5. B. Weder, J. Barzen, F. Leymann, M. Salm, and D. Vietz, “The Quantum Software Lifecycle,” in Proceedings of the 1st ACM SIGSOFT International Workshop on Architectures and Paradigms for Engineering Quantum Software (APEQS 2020), Nov. 2020, pp. 2--9. doi: 10.1145/3412451.3428497.
    6. B. Weder, U. Breitenbücher, F. Leymann, and K. Wild, “Integrating Quantum Computing into Workflow Modeling and Execution,” in 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), Dec. 2020, pp. 279–291. doi: 10.1109/UCC48980.2020.00046.
    7. E. Sood, S. Tannert, D. Frassinelli, A. Bulling, and N. T. Vu, “Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension,” in Proceedings of the 24th Conference on Computational Natural Language Learning, Online, Nov. 2020, pp. 12--25. doi: 10.18653/v1/2020.conll-1.2.
    8. E. Sood, S. Tannert, P. Mueller, and A. Bulling, “Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention,” in Advances in Neural Information Processing Systems, 2020, vol. 33, pp. 6327--6341. [Online]. Available:
    9. M. Salm, J. Barzen, U. Breitenbücher, F. Leymann, B. Weder, and K. Wild, “The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms,” in Proceedings of the 14th Symposium and Summer School on Service-Oriented Computing (SummerSOC 2020), Dec. 2020, pp. 66--85. doi: 10.1007/978-3-030-64846-6_5.
    10. M. Salm, J. Barzen, F. Leymann, and B. Weder, “About a Criterion of Successfully Executing a Circuit in the NISQ Era: What $wd 1/\epsilon_eff$ Really Means,” Nov. 2020. doi: 10.1145/3412451.3428498.
    11. P. Müller, E. Sood, and A. Bulling, “Anticipating Averted Gaze in Dyadic Interactions,” in ACM Symposium on Eye Tracking Research and Applications, Stuttgart, Germany, Jun. 2020, pp. 1–10. doi: 10.1145/3379155.3391332.
    12. J. Kneifl, D. Grunert, and J. Fehr, “A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning,” Universität Stuttgart, 2020. doi: 10.18419/OPUS-11181.
    13. F. Kempter, F. Bechler, and J. Fehr, “Calibration Approach for Muscle Activated Human Models in Pre-Crash Maneuvers with a Driver-in-the-Loop Simulator,” Skövde, Sweden, 2020. doi: 10.3233/ATDE200029.
    14. R. Diestelkämper and M. Herschel, “Tracing nested data with structural provenance for big data analytics,” in Proceedings of the International Conference on Extending Database Technology (EDBT), 2020, pp. 253–264. doi: 10.5441/002/edbt.2020.23.
    15. R. Diestelkämper and M. Herschel, “Distributed Tree-Pattern Matching in Big Data Analytics Systems,” in In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS), 2020, pp. 171–186. doi:
  4. 2019

    1. L. Harzenetter, U. Breitenbücher, F. Leymann, K. Saatkamp, B. Weder, and M. Wurster, “Automated Generation of Management Workflows for Applications Based on Deployment Models,” in 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), Oct. 2019, pp. 216–225. doi: 10.1109/EDOC.2019.00034.

Project Network Coordinators

Melanie Herschel

Prof. Dr. rer. nat.
This image shows Michael Sedlmair

Michael Sedlmair

Prof. Dr.

Professorship Virtual Reality and Augmented Reality

[Photo: SimTech/Max Kovalenko]

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