arXiv AI By Peter Steiner, Azarakhsh Jalalvand, Nathaniel Chen, Kouroche Bouchiat, Ricardo Shousha, SangKyeun Kim, Egemen Kolemen

IGNITE Tokamak World Model Architecture

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IGNITE is a generative world foundation model designed to simulate fusion plasma behavior in the DIII‑D National Fusion Facility. Trained in a self‑supervised way on more than a decade of unlabeled experimental data, its core dynamics model can predict entire discharges from specified actuator trajectories, which may be supplied or generated from textual prompts or desired outcomes. The architecture uses spatio‑temporal tokenizers for diverse input modalities—time‑series measurements, image sequences, and high‑resolution spectrograms—feeding an auto‑regressive backbone that can forecast discharges over an infinite horizon.

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TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models

arXiv:2602. 10132v3 Announce Type: replace-cross Abstract: Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors readings.

By C\'ecile Rousseau, Samuel Jackson, Rodrigo H. Ordonez-Hurtado, Nicola C. Amorisco, Tobia Boschi, George K. Holt, Andrea Loreti, Eszter Sz\'ekely, Alexander Whittle, Adriano Agnello, Stanislas Pamela, Alessandra Pascale, Robert Akers, Juan Bernabe Moreno, Sue Thorne, Mykhaylo Zayats
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arXiv:2602. 15084v2 Announce Type: replace-cross Abstract: We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous diagnostics from the publicly available MAST dataset.

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