The IAEA Fusion Data Lake Project -- Accelerating AI and Big Data Applications through Open Science and FAIR Data

TL;DR

The IAEA Fusion Data Lake Project aims to enhance AI and big data applications in fusion research by creating an open, FAIR-compliant data infrastructure with catalogues, storage, and federation to improve data accessibility.

Contribution

This work introduces a modern data platform supporting AI workflows in fusion research, integrating international data catalogues and demonstrating scalability through a proof of concept.

Findings

Successful integration with UKAEA's MAST Data Catalog

Demonstrated data federation capacity

Planned scalability with additional experimental catalogues

Abstract

AI applications in fusion is a maturing field, playing a key role as surrogate models and digital twins to overcome computational expense limitations and insufficiently characterised phenomena, and expanding the horizon for real-time applications. The IAEA is supporting this activity through the AI for Fusion Coordinated Research Project (CRP), a five-year initiative launched in 2022, which involves 24 institutions across 11 countries. A key goal is to support the development of modern data infrastructure required to enable the development of agnostic AI models that can be safely extrapolate into the parameter space of future fusion power plants. The IAEA is playing an active role in contributing to the data infrastructure with the Fusion Data Lake project. A modern data platform to enable the development of AI workflows in line with FAIR data principles. The platform comprises three major components: 1. An international data catalogue; 2. A centralised medium-term storage; and 3. A data federation of the various fusion data platforms around the world. The current proof of concept (PoC) demonstrates the data cataloguing and federation capacity by integrating with the UKAEA's MAST Data Catalog. Currently, the second phase of the PoC will demonstrate scalability by integrating two additional experimental fusion device catalogues. This report presents a high-level project overview, including: - Technical architecture and design, collaborations and contributions, and the PoC solution; - Data and metadata model development and ontological concepts; and - The approach to data governance and terms of service. This illustrates the approach, results, and direction of the work, highlighting the high potential value to the fusion community of increasing the visibility and accessibility of the numerous international experimental data sets.