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TokaLab: A Modular Virtual Tokamak Laboratory for Education, FAIR Principles, and Algorithm Benchmarking

Authors
Affiliations
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Consorzio RFX (CNR, ENEA, INFN, University of Padova, Acciaierie Venete SpA), C.so Stati Uniti 4, 35127 Padova, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy
Department of Industrial Engineering, “Tor Vergata” University of Rome, Via del Politecnico 1, 00133 Rome, Italy.

The advancement of nuclear fusion research depends not only on scientific and technological breakthroughs, but also on accessible, transparent, and reusable knowledge frameworks. To address this need, we present TokaLab, an open-access repository designed to promote education, collaboration, and the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) principles in the fusion community. TokaLab provides a suite of multi-fidelity models that enable researchers, students, and educators to explore plasma physics and fusion reactor concepts across different levels of complexity. Its modular and flexible architecture allows seamless integration of new geometries, diagnostics, and simulation tools, thereby supporting extensibility and adaptability for a wide range of applications. Beyond its educational role, TokaLab offers a unique platform for synthetic data generation and data augmentation, facilitating the development and benchmarking of algorithms like AI-based and inverse methods in fusion research. By combining educational resources, research-grade tools, and a collaborative environment, TokaLab aims at lowering the entry barrier for newcomers while fostering reproducibility, innovation, and knowledge exchange among experts. In this work, we will introduce the repository’s design, showcase example applications, and discuss its potential role in accelerating innovation, training, and AI integration in the next generation of fusion science.

Repository

https://github.com/TokaLab