Speaker
Description
Developing open, transparent, and transferable knowledge frameworks is essential for advancing plasma physics research and supporting both theoretical and experimental studies. In this context, we have developed TokaLab: an open-access virtual tokamak designed for education and research. This repository aims to foster learning, collaboration, and the adoption of the FAIR principles (Findable, Accessible, Interoperable, and Reusable) within the plasma physics community.
TokaLab features a modular and flexible architecture that enables the integration of new geometries, diagnostics, and simulation tools at various levels of complexity, rendering it easily extensible and adaptable to a wide range of applications. It serves not only as an engaging educational platform but also as a powerful resource for synthetic data generation and computational method exchange, facilitating the benchmarking and validation of algorithms, including AI-based and inverse problem approaches in thermonuclear plasma physics.
By combining educational resources, research tools, and a collaborative environment, TokaLab aims to lower entry barriers for newcomers while promoting reproducibility, innovation, and knowledge sharing among experts. In this contribution, we present the platform’s architecture, demonstrate examples of application, and explore its potential to drive innovation, training, and AI integration in the future of plasma science.
