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

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 multi-field 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. In the first funding period, we developed a general concept for the interactive visual analysis of multi-run multi-field data that considers all facets. In the second funding period, we strive for extending the concept in several aspects: To investigate the interplay of the input parameters, we want to generalize their analysis to higher-dimensional parameter spaces. Moreover, since the generation of new simulation runs is often time-consuming, we want to (approximately) predict the outcome of new simulation runs, where the uncertainties of the prediction shall be estimated and visualized, which allows for computational steering. Besides the global comparisons of spatio-temporal simulation runs, one is also interested in the spatially local correlations and causalities, whose analysis for the entire ensemble is what we also aim for now. Finally, we also want to integrate a comparative analysis of the entire simulation ensemble with measured data. These extensions build upon the already developed interactive visual analysis methods, but allow for new insights and will significantly increase the impact of our concept for multi-run multi-field data analysis.

• DFG Programme: Research Grants