Visual Analysis of Multi-run Multi-field Simulation Data

• Applicant: Professor Dr.-Ing. Lars Linsen
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2014 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 260446826

• Final Report Year: 2023

Final Report Abstract

Numerical simulations of spatio-temporal phenomena are widely used in science and engineering to test theoretical models and make predictions. Such simulations, frequently, rely on a number of input parameters or initial conditions. Since the precise settings for the input parameters are often unknown or their influence is subject to investigation, researchers execute a larger number of simulation runs with different parameter settings. Hence, the overall outcome of the simulation runs is, commonly, multi-run multi-variate time-varying volumetric simulation data (or multi-run multifield data for short). The analysis of such data is a challenge due to their complex structure with their multiple facets (i.e., multi-run, multi-variate, spatio-temporal) and also due to their large sizes going significantly beyond primary storage capacities. We developed a general concept for the interactive visual analysis of multi-run multi-field data that considers all facets. The analysis methods developed include the comparison of multiple runs, investigation of the multi-dimensional parameter space, interplay of the input parameters, detection of spatio-temporal patterns, multi-field analysis of multi-run data, linked interactive volume visualization, and prediction of simulation outcomes. Our approach facilitates the analysis of the simulated phenomena for hypothesis testing, allows for the detection of unknown features in multi-run multi-field data, provides aggregated views for hypothesis making, and supports computational steering concerning the parameter settings.

Publications

• Fast and Robust Isosurface Similarity Maps Extraction Using Quasi-Monte Carlo Approach. Analysis of Large and Complex Data, 497–506.
  Fofonov, Alexey & Linsen, Lars
  
• Visual Analysis of Multi-Run Spatio-Temporal Simulations Using Isocontour Similarity for Projected Views. IEEE Transactions on Visualization and Computer Graphics, 22(8), 2037-2050.
  Fofonov, Alexey; Molchanov, Vladimir & Linsen, Lars
  
• A Top-Down Interactive Visual Analysis Approach for Physical Simulation Ensembles at Different Aggregation Levels. Information, 9(7), 163.
  Fofonov, Alexey & Linsen, Lars
  
• MultiVisA: Visual Analysis of Multi-run Physical Simulation Data using Interactive Aggregated Plots. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 62-73.
  Fofonov, Alexey & Linsen, Lars
  
• Projected Field Similarity for Comparative Visualization of Multi‐Run Multi‐Field Time‐Varying Spatial Data. Computer Graphics Forum, 38(1), 286-299.
  Fofonov, A. & Linsen, L.
  
• Uncertainty-aware asynchronous scattered motion interpolation using Gaussian process regression. Computerized Medical Imaging and Graphics, 72, 1-12.
  Kocev, Bojan; Hahn, Horst Karl; Linsen, Lars; Wells, William M. & Kikinis, Ron
  
• Uncertainty-aware Prediction in Spatio-temporal Simulation Ensemble Visualizations. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 216-224.
  Evers, Marina & Linsen, Lars
  
• Uni- and multi-modal uncertainty visualization in 2d scalar field ensembles. In Robert Kosara, Kai Lawonn, Lars Linsen, and Noeska N. Smit, editors, 1st EuroVis Workshop on Trustworthy Visualization, TrustVis@EuroVis 2019, Porto, Portugal, June 3, 2019, pages 7–11.
  Gebauer, Eike & Linsen, Lars
  
• Aggregated Ensemble Views for Deep Water Asteroid Impact Simulations. IEEE Computer Graphics and Applications, 40(1), 72-81.
  Leistikow, Simon; Huesmann, Karim; Fofonov, Alexey & Linsen, Lars
  
• Interactive visual similarity analysis of measured and simulated multi-field tubular flow ensembles. In Barbora Kozlkova, Michael Krone, Noeska N. Smit, Kay Nieselt, and Renata Georgia Raidou, editors, 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020, Tubingen, Germany, September 28 - October 1, 2020, pages 139–150.
  Simon Leistikow, Ali Nahardani, Verena Hoerr & Lars Linsen
  
• Temporally Coherent Topological Landscapes for Time-varying Scalar Fields. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 54-61.
  Herick, Maria; Molchanov, Vladimir & Linsen, Lars
  
• Uncertainty‐aware Visualization of Regional Time Series Correlation in Spatio‐temporal Ensembles. Computer Graphics Forum, 40(3), 519-530.
  Evers, Marina; Huesmann, Karim & Linsen, Lars
  
• ASEVis: Visual Exploration of Active System Ensembles to Define Characteristic Measures. 2022 IEEE Visualization and Visual Analytics (VIS), 150-154.
  Evers, Marina; Wittkowski, Raphael & Linsen, Lars
  
• Coherent Topological Landscapes for Simulation Ensembles. Computer Vision, Imaging and Computer Graphics Theory and Applications, 223–237.
  Evers, Marina; Herick, Maria; Molchanov, Vladimir & Linsen, Lars
  
• How wildfires spread and why: Visual multi-field analysis of vorticity-driven lateral spread ensembles. In IEEE SciVis Contest, 2022
  Gabriel Borrelli; Lars Hagemann; Jannik Steinkuhler; Adrian Derstroff; Marina Evers; Karim Huesmann; Simon Leistikow; Hennes Rave; Reyhaneh Sabbagh Gol & Lars Linsen
  
• Multi-dimensional parameter-space partitioning of spatio-temporal simulation ensembles. Computers & Graphics, 104, 140-151.
  Evers, Marina & Linsen, Lars
  
• Multifaceted Visual Analysis of Oceanographic Simulation Ensemble Data. IEEE Computer Graphics and Applications, 42(4), 80-88.
  Rave, Hennes; Fincke, Johannes; Averkamp, Steffen; Tangerding, Beate; Wehrenberg, Luca P.; Gerrits, Tim; Huesmann, Karim; Leistikow, Simon & Linsen, Lars
  
• SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio‐temporal Ensembles. Computer Graphics Forum, 41(3), 379-389.
  Huesmann, Karim & Linsen, Lars
  
• Studying the effect of tissue properties on radiofrequency ablation by visual simulation ensemble analysis. In Renata G. Raidou, Bjorn Sommer, Torsten W. Kuhlen, Michael Krone, Thomas Schultz, and Hsiang-Yun Wu, editors, VCBM 2022: Eurographics Workshop on Visual Computing for Biology and Medicine, Vienna, Austria, September 22 - 23, 2022, pages 53–63.
  Karl Heimes, Marina Evers, Tim Gerrits, Sandeep Gyawali, David Sinden, Tobias Preusser & Lars Linsen
  
• Interactive visual analysis of regional time series correlation in multi-field climate ensembles. In EuroVis Workshop on Visualization in Environmental Sciences (EnvirVis), 2023
  Marina Evers; Michael Bottinger & Lars Linsen