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...
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: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:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.
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.
arXiv:2605. 14939v2 Announce Type: replace-cross Abstract: Reliable position and shape control in tokamak plasmas requires accurate real-time regulation of several strongly coupled shape parameters.
The paper reports on the integration and testing of neural‑network emulators that predict plasma shape for the MAST‑Upgrade tokamak’s plasma control system. The neural models use plasma current, poloidal field coil currents, and plasma profile parameters to output shape predictions and Jacobians, which are then used by a real‑time C++ inference server to compute virtual circuit matrices and updated coil current requests. The work emphasizes a validation workflow and best practices to build confidence in the AI‑based control framework before experimental deployment.
arXiv:2607. 11915v1 Announce Type: cross Abstract: Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data.
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.
arXiv:2606. 07550v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly and risky.
The paper presents an end‑to‑end workflow for deploying a recurrent probabilistic neural network (RPNN) on FPGA hardware to enable real‑time plasma state estimation for Tokamak control. By reducing architecture size and applying quantization‑aware training with QKeras, the model is synthesized with hls4ml for a Xilinx Alveo U50 device, achieving deterministic sub‑10 µs single‑timestep latency while staying within all four resource budgets (DSP, LUT, FF, BRAM).
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...