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    Area of Science:

    • Planetary Science
    • Computational Fluid Dynamics
    • Data Visualization

    Background:

    • Simulation ensembles, particularly for deep water asteroid impacts, generate complex datasets.
    • Analyzing these simulations involves challenges in pattern detection, multi-run comparison, and parameter influence assessment.

    Purpose of the Study:

    • To propose novel visual encodings for analyzing simulation ensembles.
    • To develop an interactive visual analysis tool for deep water asteroid impact simulations.
    • To facilitate the detection of spatiotemporal patterns and the evaluation of simulation parameters.

    Main Methods:

    • Development of multi-level aggregation techniques for simulation data.
    • Design of specific visual encodings tailored for spatiotemporal pattern detection.
    • Integration of these encodings into an interactive visual analysis platform.

    Main Results:

    • The proposed visual encodings enable effective detection of spatiotemporal patterns in simulation ensembles.
    • The interactive tool allows for straightforward comparison of multiple simulation runs.
    • Analysis of simulation parameter influence is enhanced through the developed visual methods.

    Conclusions:

    • The presented visual analysis tool and encodings significantly improve the understanding of deep water asteroid impact simulations.
    • This approach provides a robust framework for analyzing complex simulation ensembles in planetary science.
    • Interactive visualization is crucial for extracting meaningful insights from large-scale scientific simulations.