Advanced Techniques for Fusion Data Visualisation

Nitesh Bhatia ^* Rui Costa Samuel Jackson Nathan Cummings Stanislas Pamela Shaun de-Witt Alejandra N Gonzalez-Beltran Rob Akers

• United Kingdom Atomic Energy Authority, Abingdon, England, United Kingdom

The increasing complexity of fusion research, which encompasses diverse data types from engineering CAD models to multi-physics simulations and AI-driven diagnostics and predictions, calls for the creation of advanced visualisation systems. This study investigates how advanced visualisation might be adapted to work with fusion data, enhancing usability and integration. This study focuses on three fundamental domains. First, efficient analysis of complex information comprising merging and instinctive exploration of heterogeneous fusion data sets. Second, advanced visualisation pipelines incorporating heterogeneous data, with iterative enhancement and retrospective investigation. Finally, the possibilities of new tools and technologies for exploring fusion-specific use cases, including 3D visualisation, dashboards and immersive technologies, are discussed. As a result, this paper presents an integrative approach to combining diverse fusion data sources using advanced tools such as NVIDIA Omniverse, ParaView, Blender, Grafana, and WebXR. We further discuss a framework integrating simulation data, diagnostics, and design models into an interactive ecosystem. We demonstrate its effectiveness through key use cases, including camera-like MHD simulations, interactive diagnostic dashboards, and immersive AR/VR visualisation of tokamak data. These advances enhance scientific understanding, facilitate cross-disciplinary collaboration, and pave the way for future AI-driven adaptive visualisation in fusion research.