Machine learning for data-driven visualization (ML4Vis)

PN 6-6

Project description

This project addresses the research problem of using machine learning (ML) for visualization (ML4Vis). We will develop visualization methods that employ a data-driven approach for feature specification and extraction in the context of multifield data. For this, we will especially use Neural Network architectures and formulate our visualization tasks as supervised learning problems. Our visualization presents identified characteristics of interest when applying trained models to the data, or uses the models for selecting and directly generating expressive visual representations for data analysis. This research directly tackles a fundamental challenge in visualization: meaningful data reduction and abstraction are required, yet solely relying on manual specification by the user is increasingly impractical considering the growing data size and complexity from simulations and experiments. The developed approaches will be applied together with SimTech collaborators to help them analyze, understand, and discover new characteristics in their data.

Project information

Project title Machine Learning for Data-driven Visualization (ML4Vis)
Project leaders Thomas Ertl (Ingo Steinwart)
Project duration January 2019 - June 2022
Project number PN 6-6

Publications PN 6-6

  1. 2023

    1. 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.
    2. 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.
    3. 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.
    4. L. Pfitzer, J. Heitkämper, J. Kästner, and R. Peters, “Use of the N–O Bonds in N-Mesyloxyamides and N-Mesyloxyimides To Gain Access to 5-Alkoxy-3,4-dialkyloxazol-2-ones and 3-Hetero-Substituted Succinimides: A Combined Experimental and Theoretical Study,” Synthesis, vol. 55, no. 26, Art. no. 26, 2023, doi: 10.1055/s-0042-1751447.
  2. 2022

    1. 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.
    2. 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.
    3. 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.
  3. 2021

    1. 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.
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