Agent Based In-Situ Visualization by Guide Field

Yan Wang, Akira Kageyama

2023

Abstract

In situ visualization has become an important research method today in high performance computing. In our previous study, we proposed 4D Street View (4DSV), in which multiple visualization cameras are scattered in the simulation region for interactive analysis of visualization video files after the simulation. A challenge in the 4DSV approach is to increase the camera density around a local area of the simulation box for detailed visualizations. To make the cameras automatically identify such a local region or Volume of Interest (VOI), we propose introducing the concept of a swarm of visualization cameras, which is an application of agent-based modeling to in-situ visualization. The camera agents in the camera swarm are autonomous entities. They find VOIs by themselves and communicate with each other through a virtual medium called a visualization guide field that is distributed in the simulation space.

Paper Citation

in Harvard Style

Wang Y. and Kageyama A. (2023). Agent Based In-Situ Visualization by Guide Field. In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-668-2, SciTePress, pages 332-339. DOI: 10.5220/0012092800003546

in Bibtex Style

@conference{simultech23,
author={Yan Wang and Akira Kageyama},
title={Agent Based In-Situ Visualization by Guide Field},
booktitle={Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2023},
pages={332-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012092800003546},
isbn={978-989-758-668-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Agent Based In-Situ Visualization by Guide Field
SN - 978-989-758-668-2
AU - Wang Y.
AU - Kageyama A.
PY - 2023
SP - 332
EP - 339
DO - 10.5220/0012092800003546
PB - SciTePress