Tokamak-SI is a unified, time‑aligned, categorical dataset that aggregates labels and labeling methods from numerous prior studies to identify phenomena in DIII‑D tokamak discharges, including confinement, detachment, AE, ELM, sawtooth, TM, and resistive wall modes. Models trained on Tokamak-SI can extend labeling to new shots and match or outperform earlier detectors for AE, ELM, and TM detection. The dataset is designed to support task‑specific benchmarking, physics‑searchable archives, and direct event identification during experiments.
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.
arXiv:2609.24443v1 Announce Type: cross Abstract: Reliable disruption prediction is essential for the safe operation of future tokamaks. Existing full-distribution survival methods model the complete...
arXiv:2607. 11915v1 Announce Type: cross Abstract: Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data.
arXiv:2611.08153v1 Announce Type: new Abstract: Tokamak experiments record diverse measurements, including waveforms, profiles, fields, and scalars, which are used to reconstruct the plasma, forecast...
arXiv:2609.26095v1 Announce Type: cross Abstract: With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the...
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.
The paper presents a probabilistic neural surrogate for scrape‑off‑layer (SOL) plasma simulations in tokamaks. By mapping the curvilinear SOLPS‑ITER mesh to three fixed‑size image tensors, the authors preserve geometric adjacency and enable a convolutional network to process the mesh without loss of information. A conditional flow‑matching model is trained on this representation, producing efficient, scalable predictions that capture multiple plausible outcomes—such as distinct hot and cold modes—near the divertor detachment transition and correctly recover known bifurcations in synthetic data.
Fast surrogates for tokamak boundary-plasma simulation are typically deterministic regressors mapping a global operating point to a flattened vector of cell values. Near the divertor detachment transi...
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.
arXiv:2607. 22704v1 Announce Type: cross Abstract: Nuclear fusion has made significant progress in recent years and is expected to become one of the most important pathways to addressing global energy challenges.
arXiv:2607. 21407v1 Announce Type: cross Abstract: The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset of detachment.
arXiv:2608.23217v1 Announce Type: cross Abstract: Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solver...