Conveners
Information Retrieval and Visualisation
- Geert Verdoolaege (UGent)
Information Retrieval and Visualisation
- Geert Verdoolaege (UGent)
This report focuses on technological innovations in visualization within magnetic confinement fusion research, systematically elaborating on the groundbreaking applications of cinematic dynamic simulation, extended reality (XR) interaction, and intelligent 3D reconstruction in device modeling, theoretical demonstration, and scientific communication.
Based on high spatiotemporal resolution...
Understanding plasma behavior throughout the entire duration of a pulse is critical for achieving the objectives of ITER and future fusion devices. Plasma parameters such as temperature, density, current profiles, and impurity content evolve dynamically during a discharge, influencing key aspects of performance including energy confinement, stability, and fusion power production. Analyzing...
The increasing integration of artificial intelligence (AI) into fusion research demands scalable, standardized, and traceable data infrastructures. To address this need, we introduce JDDB (J-TEXT Disruption Database), a flexible and extensible most of all, a light weight data processing framework designed to streamline the management and transformation of fusion data across tokamak...
It is well known that ITER utilizes IMAS for data storage and exchanging between experiments and simulations. The software infrastructure of the synthetic diagnostic platform (SDP) on EAST is based on IMAS. To enable the visualization of the data on SDP through web,a RESTful API has been developed. Benefiting from the mature web technologies, it is also possible to easily integrate the user...
The cornerstone of the Integrated Modelling & Analysis Suite (IMAS) developed by ITER is its machine-agnostic data model, called the Data Dictionary (DD). It was recently made open access [1] to permit a broader adoption outside of the ITER Members and by private fusion ventures. Discussions and contributions are welcome and held publicly in GitHub, either for new extensions, clarifications or...
Data aggregation across multiple fusion devices has enormous value for improving machine learning models and for validating simulation tools. One challenge in forming and using such datasets can simply be the latency caused by the distance between experimental sites and the computational facilities where data is used. Other challenges arise from the different data access interfaces exposed by...
