In Situ Adaptive Timestep Control and Visualization Based on the Spatio-Temporal Variations of the Simulation Results
Event Type
Registration Categories
Data Analytics
Scientific Computing
TimeMonday, 18 November 20199:42am - 10am
DescriptionEffective visualization of time-varying volumetric data is considered challenging, since in addition to the traditional visualization parameter selections, there is also the need to take care about the dynamic changings between the timesteps. In situ adaptive sampling control based on the amount of change between the simulation timepsteps might be helpful for the I/O and visualization cost savings. In this paper, we propose the use of Kernel Density Estimation (KDE) and Kullback-Leibler (KL) divergence, in order to estimate the amount of internal variations between the timesteps, and use this parameter for controlling the sampling rate of the timesteps. Needless to say that these selected timesteps should be sufficient to understand the underlying phenomena without missing important data features. We confirmed the effectiveness on reducing the number of timepsteps, by using a CFD simulation of irregular volume data by using OpenFOAM. However, we could also observe that some parameter selection highly influences the visualization smoothness, and this remains as a future work.
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