Publications of PN 7

Publications PN 7

  1. 2024

    1. J. Meißner, D. Göddeke, and M. Herschel, Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations. in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). 2024.
    2. J. Kneifl, J. Fehr, S. L. Brunton, and J. N. Kutz, “Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks.” 2024.
    3. B. Flemisch et al., “Research Data Management in Simulation Science: Infrastructure, Tools, and Applications,” Datenbank-Spektrum, 2024, doi: https://doi.org/10.1007/s13222-024-00475-4.
  2. 2023

    1. X. Yu, “DC Limb Motion Guidance in Extended Reality,” in 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), in 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). Mar. 2023, pp. 967–968. doi: 10.1109/VRW58643.2023.00326.
    2. N. Hube, M. Reinelt, K. Vidackovic, and M. Sedlmair, “Work vs. Leisure – Differences in Avatar Characteristics Depending on Social Situations,” in Proceedings of the 16th International Symposium on Visual Information Communication and Interaction (VINCI ’23), in Proceedings of the 16th International Symposium on Visual Information Communication and Interaction (VINCI ’23). Association for Computing Machinery, 2023. doi: https://doi.org/10.1145/3615522.3615537.
    3. P. Gebhardt et al., “Auxiliary Means to Improve Motion Guidance Memorability in Extended Reality,” in 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), in 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). Mar. 2023, pp. 689–690. doi: 10.1109/VRW58643.2023.00187.
    4. F. Grioui and T. Blascheck, “Heart Rate Visualizations on a Virtual Smartwatch to Monitor Physical Activity Intensity,” in Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, in Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications, 2023. doi: 10.5220/0011665500003417.
    5. J. Haischt and M. Sedlmair, “What’s (Not) Tracking? Factors of Influence in Industrial Augmented Reality Tracking: A Use Case Study in an Automotive Environment,” in Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, in Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. Ingolstadt, Germany: Association for Computing Machinery, Sep. 2023, pp. 42–51. doi: 10.1145/3580585.3607156.
    6. M. Wieland, M. Sedlmair, and T.-K. Machulla, “VR, Gaze, and Visual Impairment: An Exploratory Study of the Perception of Eye Contact across different Sensory Modalities for People with Visual Impairments in Virtual Reality,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany: Association for Computing Machinery, Apr. 2023, pp. 1–6. doi: 10.1145/3544549.3585726.
    7. S. Rigling, X. Yu, and M. Sedlmair, “‘In Your Face!’: Visualizing Fitness Tracker Data in Augmented Reality,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany: Association for Computing Machinery, Apr. 2023, pp. 1–7. doi: 10.1145/3544549.3585912.
    8. A. Schmitz-Hübsch, R. Becker, and M. Wirzberger, “Personality Traits in the Emotion-Performance-Relationship in Intelligent Tutoring Systems,” in Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science, in Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science. , Springer, 2023, pp. 60–75. doi: 10.1007/978-3-031-34735-1_5.
    9. P. Rodegast, S. Maier, J. Kneifl, and J. Fehr, “Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios.” DaRUS, 2023. doi: 10.18419/DARUS-3301.
    10. J. Kneifl and J. Fehr, “Crash Simulations of a Racing Kart’s Structural Frame Colliding against a Rigid Wall.” DaRUS, 2023. doi: 10.18419/DARUS-3789.
    11. L. R. Skreinig et al., “guitARhero: Interactive Augmented Reality Guitar Tutorials,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–10, 2023, doi: 10.1109/TVCG.2023.3320266.
    12. A. R. Nikolaev, B. V. Ehinger, R. N. Meghanathan, and C. van Leeuwen, “Planning to revisit: Neural activity in refixation precursors,” Journal of Vision, vol. 23, no. 7, Art. no. 7, Jul. 2023, doi: 10.1167/jov.23.7.2.
    13. J. Hay et al., “Application of data-driven surrogate models for active human model response prediction and restraint system optimization,” Frontiers in applied mathematics and statistics, vol. 9, pp. 1–16, 2023, doi: 10.3389/fams.2023.1156785.
    14. H. Bonasch and B. V. Ehinger, “Decoding accuracies as well as ERP amplitudes do not show between-task correlations,” Conference on Cognitive Computational Neuroscience, 2023, doi: 10.32470/CCN.2023.1029-0.
    15. R. Frömer, M. R. Nassar, B. V. Ehinger, and A. Shenhav, “Common neural choice signals emerge artifactually amidst multiple distinct value signals,” bioRxiv, 2023, doi: 10.1101/2022.08.02.502393.
    16. R. S. Skukies and B. Ehinger, “The effect of estimation time window length on overlap correction in EEG data,” Conference on Cognitive Computational Neuroscience, 2023, doi: 10.32470/CCN.2023.1229-0.
    17. J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. Fehr, “Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction,” Archive of Applied Mechanics, vol. 93, pp. 3637–3663, 2023, doi: 10.1007/s00419-023-02458-5.
    18. C. Yan, B. Ehinger, A. Pérez-Bellido, M. V. Peelen, and F. P. de Lange, “Humans predict the forest, not the trees : statistical learning of  spatiotemporal structure in visual scenes,” Cerebral cortex, vol. 33, no. 13, Art. no. 13, 2023, doi: 10.1093/cercor/bhad115.
  3. 2022

    1. 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. in Proceedings of the 15th International Symposium on Visual Information Communication and Interaction. New York, NY, USA: Association for Computing Machinery, 2022, pp. 1–5. doi: 10.1145/3554944.3554956.
    2. A. Sousa Calepso, N. Hube, N. Berenguel Senn, V. Brandt, and M. Sedlmair, “cARdLearner: Using Expressive Virtual Agents when Learning Vocabulary in Augmented Reality,” in ACM Conference on Human Factors in Computing Systems Extended Abstracts (CHI-EA)), in ACM Conference on Human Factors in Computing Systems Extended Abstracts (CHI-EA)). New Orleans, LA, USA, 2022. doi: 10.1145/3491101.3519631.
    3. L. R. Skreinig et al., “AR Hero: Generating Interactive Augmented Reality Guitar Tutorials,” in 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), in 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). Mar. 2022, pp. 395–401. doi: 10.1109/VRW55335.2022.00086.
    4. N. Hube, K. Vidackovic, and M. Sedlmair, “Using Expressive Avatars to Increase Emotion Recognition: A Pilot Study,” in Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. New Orleans, LA, USA: Association for Computing Machinery, Apr. 2022, pp. 1–7. doi: 10.1145/3491101.3519822.
    5. S. Oppold and M. Herschel, “Provenance-based explanations: are they useful?,” in International Workshop on the Theory and Practice  of Provenance (TAPP), in International Workshop on the Theory and Practice  of Provenance (TAPP). 2022, pp. 2:1--2:4. doi: 10.1145/3530800.3534529.
    6. J. Eirich, M. Münch, D. Jäckle, M. Sedlmair, J. Bonart, and T. Schreck, “RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines,” Computer Graphics Forum, vol. 41, no. 6, Art. no. 6, Mar. 2022, doi: 10.1111/cgf.14452.
    7. Q. Zhou, J. Fehr, D. Bestle, and X. Rui, “Simulation of generally shaped 3D elastic body dynamics with large motion using transfer matrix method incorporating model order reduction,” Multibody System Dynamics, vol. 59, no. 3, Art. no. 3, 2022, doi: 10.1007/s11044-022-09869-2.
    8. J. Nicodemus, J. Kneifl, J. Fehr, and B. Unger, “Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,” IFAC-PapersOnLine, vol. 55, no. 20, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.117.
    9. A. L. Gert, B. V. Ehinger, S. Timm, T. C. Kietzmann, and P. König, “WildLab: A naturalistic free viewing experiment reveals previously unknown electroencephalography signatures of face processing,” European Journal of Neuroscience, vol. 56, no. 11, Art. no. 11, Nov. 2022, doi: 10.1111/ejn.15824.
    10. S. Hermann and J. Fehr, “Documenting research software in engineering science,” Scientific Reports, vol. 12, no. 1, Art. no. 1, Apr. 2022, doi: 10.1038/s41598-022-10376-9.
  4. 2021

    1. F. Grioui and T. Blascheck, Study of Heart Rate Visualizations on a Virtual Smartwatch. in Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology. ACM, 2021. doi: https://doi.org/10.1145/3489849.3489913.
    2. 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,” in In Proceedings of the ACM SIG Conference on the Management of Data (SIGMOD), in In Proceedings of the ACM SIG Conference on the Management of Data (SIGMOD). 2021.
    3. J. Kühnert, D. Göddeke, and M. Herschel, “Provenance-integrated parameter selection and optimization in numerical simulations,” in International Workshop on the Theory and Practice of Provenance (TAPP), in International Workshop on the Theory and Practice of Provenance (TAPP). USENIX Association, 2021.
    4. A. Czeszumski et al., “Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring,” Frontiers in Neurorobotics, vol. 15, Aug. 2021, doi: 10.3389/fnbot.2021.686010.
    5. 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.
    6. 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.
  5. 2020

    1. 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), in Proceedings of the International Conference on Extending Database Technology (EDBT). 2020, pp. 253–264. doi: 10.5441/002/edbt.2020.23.
    2. 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), in In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS). Springer, 2020, pp. 171–186. doi: https://doi.org/10.1007/978-3-030-54832-2_14.

Software PN 7

  1. 2024

    1. P. L. Reiser, J. E. Aguilar, A. Guthke, and P.-C. Bürkner, “Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference.” 2024. doi: 10.18419/darus-4093.
  2. 2023

    1. A. Baier and D. Frank, “deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning.” 2023. doi: 10.18419/darus-3455.
    2. F. Kempter, L. Lantella, N. Stutzig, J. C. Fehr, and T. Siebert, “Neck Reflex Behavior in Driving Simulator Experiments - Academic-Scale Simulator at ITM.” 2023. doi: 10.18419/darus-3000.
    3. J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. C. Fehr, “Continuum-mechanical Forward Simulation Results of a Human Upper-limb Model Under Varying Muscle Activations.” 2023. doi: 10.18419/darus-3302.
    4. R. S. Skukies, “2023 CCN Time Window Project Code.” 2023. doi: 10.18419/darus-3635.
  3. 2022

    1. J. Kneifl, J. Hay, and J. Fehr, “Human Occupant Motion in Pre-Crash Scenario.” 2022. doi: 10.18419/darus-2471.

Data PN 7

  1. 2023

    1. J. Rettberg et al., “Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2023. doi: 10.18419/darus-3248.

Project Network Coordinators

This image shows Melanie Herschel

Melanie Herschel

Prof. Dr. rer. nat.

Data Engineering

This image shows Michael Sedlmair

Michael Sedlmair

Prof. Dr.

Virtual Reality and Augmented Reality

[Photo: SimTech/Max Kovalenko]

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