Visual Analysis of Multi-run Multi-field Simulation Data

Basic data for this project

Description

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. Visualization has proven to be helpful in analyzing certain facets of multi-run multi-field data, but the interactive visual analysis of such data considering all facets remains a challenge. We propose a general concept to tackling all facets of multi-run-multi-field data visualization including the comparison of multiple runs, investigation of the parameter space, detection of spatio-temporal patterns, multi-field analysis of multi-run data, and linked interactive volume visualization. We propose multiple coordinated views for the multiple facets, which allows for a top-down analysis of entire multi-run data sets as well as for detailed analyses on filtered views. 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.