TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics

• URL: http://arxiv.org/abs/2602.15084v1
• Date: Mon, 16 Feb 2026 12:26:07 GMT
• Title: TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
• Authors: Tobia Boschi, Andrea Loreti, Nicola C. Amorisco, Rodrigo H. Ordonez-Hurtado, Cécile Rousseau, George K. Holt, Eszter Székely, Alexander Whittle, Samuel Jackson, Adriano Agnello, Stanislas Pamela, Alessandra Pascale, Robert Akers, Juan Bernabe Moreno, Vassil Alexandrov, Mykhaylo Zayats,
• Abstract summary: TokaMind is an open-source foundation model framework for fusion plasma modeling.<n>It is trained on heterogeneous tokamak diagnostics from the publicly available MAST dataset.<n>We evaluate TokaMind on the recently introduced MAST benchmark TokaMark.
• Score: 56.073642366268764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present TokaMind, an open-source foundation model framework for fusion plasma modeling, based on a Multi-Modal Transformer (MMT) and trained on heterogeneous tokamak diagnostics from the publicly available MAST dataset. TokaMind supports multiple data modalities (time-series, 2D profiles, and videos) with different sampling rates, robust missing-signal handling, and efficient task adaptation via selectively loading and freezing four model components. To represent multi-modal signals, we use a training-free Discrete Cosine Transform embedding (DCT3D) and provide a clean interface for alternative embeddings (e.g., Variational Autoencoders - VAEs). We evaluate TokaMind on the recently introduced MAST benchmark TokaMark, comparing training and embedding strategies. Our results show that fine-tuned TokaMind outperforms the benchmark baseline on all but one task, and that, for several tasks, lightweight fine-tuning yields better performance than training the same architecture from scratch under a matched epoch budget. These findings highlight the benefits of multi-modal pretraining for tokamak plasma dynamics and provide a practical, extensible foundation for future fusion modeling tasks. Training code and model weights will be made publicly available.

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