Publications of PN 6

Publications PN 6

  1. 2023

    1. D. Holzmüller, “Regression from linear models to neural networks: double descent, active learning, and sampling,” University of Stuttgart, 2023.
    2. T. Munz-Körner, S. Künzel, and D. Weiskopf, “Supplemental Material for ‘Visual-Explainable AI: The Use Case of Language Models.’” 2023. doi: 10.18419/darus-3456.
    3. J. Cervino, L. F. O. Chamon, B. D. Haeffele, R. Vidal, and A. Ribeiro, “Learning Globally Smooth Functions on Manifolds,” in International Conference on Machine Learning~(ICML), in International Conference on Machine Learning~(ICML). 2023.
    4. N. Schäfer et al., “Visual Analysis of Scene-Graph-Based Visual Question Answering,” in Proceedings of the 16th International Symposium on Visual Information Communication and Interaction, in Proceedings of the 16th International Symposium on Visual Information Communication and Interaction. Guangzhou, China: Association for Computing Machinery, Oct. 2023, pp. 1–8. doi: 10.1145/3615522.3615547.
    5. D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart, “A Framework and Benchmark for Deep Batch Active Learning for Regression,” Journal of Machine Learning Research, vol. 24, no. 164, Art. no. 164, 2023, [Online]. Available: http://jmlr.org/papers/v24/22-0937.html
    6. V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner, “Transfer learning for chemically accurate interatomic neural network potentials,” Physical Chemistry Chemical Physics, vol. 25, no. 7, Art. no. 7, 2023, doi: 10.1039/D2CP05793J.
    7. R. Bauer et al., “Visual Ensemble Analysis of Fluid Flow in Porous Media Across Simulation Codes and Experiment,” Transport in Porous Media, 2023, doi: 10.1007/s11242-023-02019-y.
    8. J. Gödeke and G. Rigaud, “Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods,” Inverse Problems, vol. 39, no. 3, Art. no. 3, Feb. 2023, doi: 10.1088/1361-6420/acb2ed.
    9. B. N. Hahn, G. Rigaud, and R. Schmähl, “A class of regularizations based on nonlinear isotropic diffusion for inverse problems,” IMA Journal of Numerical Analysis, Feb. 2023, doi: 10.1093/imanum/drad002.
    10. A. Straub, G. K. Karch, J. Steigerwald, F. Sadlo, B. Weigand, and T. Ertl, “Visual Analysis of Interface Deformation in Multiphase Flow,” Journal of Visualization, vol. 26, no. 6, Art. no. 6, 2023, doi: 10.1007/s12650-023-00939-x.
    11. A. Straub, N. Karadimitriou, G. Reina, S. Frey, H. Steeb, and T. Ertl, “Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs,” IEEE Transactions on Visualization and Computer Graphics, 2023, doi: 10.1109/TVCG.2023.3326931.
    12. J. Rettberg et al., “Port-Hamiltonian fluid–structure interaction modelling and structure-preserving model order reduction of a classical guitar,” Mathematical and Computer Modelling of Dynamical Systems, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1080/13873954.2023.2173238.
    13. K. Gubaev, V. Zaverkin, P. Srinivasan, A. I. Duff, J. Kästner, and B. Grabowski, “Performance of two complementary machine-learned potentials in modelling chemically complex systems,” NPJ Computational Materials, vol. 9, p. 129, 2023, doi: 10.1038/s41524-023-01073-w.

  2. 2022

    1. J. Potyka et al., “Towards DNS of Droplet-Jet Collisions of Immiscible Liquids with FS3D,” High Performance Computing in Science and Engineering ’22, Springer International Publishing, 2022. [Online]. Available: https://arxiv.org/abs/2212.09727
    2. R. Leiteritz, P. Buchfink, B. Haasdonk, and D. Pflüger, “Surrogate-data-enriched Physics-Aware Neural Networks,” in Proceedings of the Northern Lights Deep Learning Workshop 2022, in Proceedings of the Northern Lights Deep Learning Workshop 2022, vol. 3. Mar. 2022. doi: 10.7557/18.6268.
    3. G. Tkachev, R. Cutura, M. Sedlmair, S. Frey, and T. Ertl, “Metaphorical Visualization: Mapping Data to Familiar Concepts,” in CHI Conference on Human Factors in Computing Systems Extended Abstracts, in CHI Conference on Human Factors in Computing Systems Extended Abstracts. ACM, Apr. 2022. doi: 10.1145/3491101.3516393.
    4. B. Xiong, S. Zhu, N. Potyka, S. Pan, C. Zhou, and S. Staab, “Pseudo-Riemannian Graph Convolutional Networks,” in Advances in Neural Information Processing Systems, in Advances in Neural Information Processing Systems. 2022. [Online]. Available: https://arxiv.org/abs/2106.03134
    5. A. Straub, S. Boblest, G. K. Karch, F. Sadlo, and T. Ertl, “Droplet-Local Line Integration for Multiphase Flow,” in 2022 IEEE Visualization and Visual Analytics (VIS), in 2022 IEEE Visualization and Visual Analytics (VIS). 2022, pp. 135–139. doi: 10.1109/VIS54862.2022.00036.
    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. K. Pluhackova, V. Schittny, P.-C. Bürkner, C. Siligan, and A. Horner, “Multiple pore lining residues modulate water permeability of GlpF,” Protein Science, vol. 31, no. 10, Art. no. 10, 2022, doi: https://doi.org/10.1002/pro.4431.
    8. V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Exploring chemical and conformational spaces by batch mode deep active learning,” Digital Discovery, vol. 1, pp. 605–620, 2022, doi: 10.1039/D₂DD00034B.
    9. C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, and G. Rigaud, “In Focus - hybrid deep learning approaches to the HDC2021 challenge,” Inverse Problems and Imaging, vol. 0, no. 0, Art. no. 0, 2022, doi: 10.3934/ipi.2022061.
    10. S. Frey et al., “Visual Analysis of Two-Phase Flow Displacement Processes in Porous Media,” Computer graphics forum, vol. 41, no. 1, Art. no. 1, 2022, doi: 10.1111/cgf.14432.
    11. D. Holzmüller and I. Steinwart, “Training two-layer ReLU networks with gradient descent is inconsistent,” Journal of Machine Learning Research, vol. 23, no. 181, Art. no. 181, 2022, [Online]. Available: http://jmlr.org/papers/v23/20-830.html
    12. V. Zaverkin, D. Holzmüller, R. Schuldt, and J. Kästner, “Predicting properties of periodic systems from cluster data: A case study of liquid water,” The Journal of Chemical Physics, vol. 156, no. 11, Art. no. 11, 2022, doi: 10.1063/5.0078983.
    13. V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,” Journal of Chemical Theory and Computation, vol. 18, pp. 1–12, 2022, doi: 10.1021/acs.jctc.1c00853.

  3. 2021

    1. T. Munz, D. Väth, P. Kuznecov, T. Vu, and D. Weiskopf, “Visual-Interactive Neural Machine Translation,” in Graphics Interface 2021, in Graphics Interface 2021. 2021. [Online]. Available: https://openreview.net/forum?id=DQHaCvN9xd
    2. D. Holzmüller and D. Pflüger, “Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework,” in Sparse Grids and Applications - Munich 2018, H.-J. Bungartz, J. Garcke, and D. Pflüger, Eds., in Sparse Grids and Applications - Munich 2018. Cham: Springer International Publishing, 2021, pp. 69--100.
    3. D. Holzmüller, “On the Universality of the Double Descent Peak in Ridgeless Regression,” in International Conference on Learning Representations, in International Conference on Learning Representations. 2021. [Online]. Available: https://openreview.net/forum?id=0IO5VdnSAaH
    4. A. Straub, G. K. Karch, F. Sadlo, and T. Ertl, “Implicit Visualization of 2D Vector Field Topology for Periodic Orbit Detection,” in Topological Methods in Data Analysis and Visualization VI, I. Hotz, T. Bin Masood, F. Sadlo, and J. Tierny, Eds., in Topological Methods in Data Analysis and Visualization VI. , Springer International Publishing, 2021, pp. 159–180. doi: 10.1007/978-3-030-83500-2_9.
    5. R. Garcia, T. Munz, and D. Weiskopf, “Visual analytics tool for the interpretation of hidden states in recurrent neural networks,” Visual Computing for Industry, Biomedicine, and Art, vol. 4, no. 24, Art. no. 24, Sep. 2021, doi: 10.1186/s42492-021-00090-0.
    6. V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” Journal of Chemical Theory and Computation, vol. 17, no. 10, Art. no. 10, 2021, doi: 10.1021/acs.jctc.1c00527.
    7. V. Zaverkin and J. Kästner, “Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design,” Machine Learning: Science and Technology, vol. 2, no. 3, Art. no. 3, 2021.

  4. 2020

    1. T. Munz, N. Schaefer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf, “Demo of a Visual Gaze Analysis System for Virtual Board Games,” in ACM Symposium on Eye Tracking Research and Applications, in ACM Symposium on Eye Tracking Research and Applications. Stuttgart, Germany: Association for Computing Machinery, 2020. doi: 10.1145/3379157.3391985.
    2. F. Heyen et al., “ClaVis: An Interactive Visual Comparison System for Classifiers,” in Proceedings of the International Conference on Advanced Visual Interfaces, in Proceedings of the International Conference on Advanced Visual Interfaces. Salerno, Italy: Association for Computing Machinery, 2020. doi: 10.1145/3399715.3399814.
    3. G. Molpeceres, V. Zaverkin, and J. Kästner, “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc., vol. 499, pp. 1373–1384, 2020, doi: 10.1093/mnras/staa2891.
    4. V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” Journal of Chemical Theory and Computation, vol. 16, pp. 5410–5421, 2020, doi: 10.1021/acs.jctc.0c00347.
    5. D. F. B. Häufle, I. Wochner, D. Holzmüller, D. Drieß, M. Günther, and S. Schmitt, “Muscles reduce neuronal information load : quantification of control effort in biological vs. robotic pointing and walking,” Frontiers in Robotics and AI, vol. 7, p. 77, 2020, doi: 10.3389/frobt.2020.00077.
    6. T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf, “Comparative Visual Gaze Analysis for Virtual Board Games,” The 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020), 2020, doi: 10.1145/3430036.3430038.

  5. 2019

    1. T. Munz, M. Burch, T. van Benthem, Y. Poels, F. Beck, and D. Weiskopf, “Overlap-Free Drawing of Generalized Pythagoras Trees for Hierarchy Visualization,” in 2019 IEEE Visualization Conference (VIS), in 2019 IEEE Visualization Conference (VIS). Oct. 2019, pp. 251–255. doi: 10.1109/VISUAL.2019.8933606.
    2. T. Munz, L. L. Chuang, S. Pannasch, and D. Weiskopf, “VisME: Visual microsaccades explorer,” Journal of Eye Movement Research, vol. 12, no. 6, Art. no. 6, Dec. 2019, doi: 10.16910/jemr.12.6.5.

Software PN 6

  1. 2023

    1. N. Schäfer et al., “Visual Analysis System for Scene-Graph-Based Visual Question Answering.” 2023. doi: 10.18419/darus-3589.

  2. 2022

    1. T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf, “NMTVis - Extended Neural Machine Translation Visualization System.” 2022. doi: 10.18419/darus-2124.

Data PN 6

  1. 2023

    1. T. Munz-Körner, S. Künzel, and D. Weiskopf, “Supplemental Material for ‘Visual-Explainable AI: The Use Case of Language Models.’” 2023. doi: 10.18419/darus-3456.
    2. N. Schäfer et al., “Model Parameters and Evaluation Data for our Visual Analysis System for Scene-Graph-Based Visual Question Answering.” 2023. doi: 10.18419/darus-3597.

  2. 2021

    1. T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf, “NMTVis - Trained Models for our Visual Analytics System.” DaRUS, 2021. doi: 10.18419/DARUS-1850.
    2. T. Munz, R. Garcia, and D. Weiskopf, “Visual Analytics System for Hidden States in Recurrent Neural Networks.” DaRUS, 2021. doi: 10.18419/DARUS-2052.

  3. 2020

    1. T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf, “Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games.” DaRUS, 2020. doi: 10.18419/DARUS-1130.

Project Network Coordinators

This image shows Dirk Pflüger

Dirk Pflüger

Prof. Dr. rer. nat.

Scientific Computing | Vice-Head of GS SimTech

[Photo: SimTech/Max Kovalenko]

This image shows Ingo Steinwart

Ingo Steinwart

Univ.-Prof. Dr. rer. nat.

Stochastics

[Photo: SimTech/Max Kovalenko]

Mathias Niepert

Prof. Dr.

Machine Learning for Simulation Science

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