Papers for

tokamak control engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Neural operator reconstructs unseen electron density in tokamaks fast and with uncertainty

Reconstruction of Unobserved Core Electron Density in Tokamak Plasmas with a Neural Operator Transport Surrogate and Uncertainty Quantification

Abstract: Real-time control of tokamak plasmas requires the electron density profile. However, in a reactor-relevant device, diagnostic port access is limited and the profile can be observed only partially. In the present work we reconstruct the density profile by inferring the coefficients of a physics-based one-dimensional particle transport equation, using a Fourier Neural Operator (FNO) surrogate of the transport solver as a fast differentiable forward model. The surrogate enables realtime-compatible gradient-based reconstruction and uncertainty quantification from the reflectometry and interferometry measurements. We compare four uncertainty quantification methods - multi-seed ensembles, the Laplace approximation, Metropolis-Hastings, and a Gaussian method independent of the reconstruction fit. The resulting uncertainty is scored with CRPS and coverage calibration, separately on the observed and unobserved regions. We further demonstrate advantage of our approach over standard analytical function fit on synthetic and experimental DIII-D discharges.

Sat 17 OctMachine Learning
The gist
Measuring the full electron density inside fusion devices called tokamaks is difficult because sensors can only see parts of it. The authors show a way to fill in the invisible parts by using a physics-based equation combined with a fast neural network model that predicts how electrons move. This method quickly estimates the full electron density and includes an assessment of how confident those estimates are. It performs better than standard curve-fitting methods on both simulated and real fusion data.
Open → 2610.24525v1

Real-time plasma state estimation runs on FPGA hardware

Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks

Abstract: Real time plasma state estimation for control of Tokamak devices are challenging due to the stringent latency requirements of the plasma control system (PCS). We present an end-to-end workflow for deploying a recurrent probabilistic neural network (RPNN) on FPGA hardware. We combine architecture size reduction with quantization-aware training via QKeras. The model is then synthesized using hls4ml, targeting a Xilinx Alveo U50 device. We report a design that fits comfortably within all four resource budgets (DSP, LUT, FF, BRAM) at deterministic sub-10~$μ$s single-timestep latency, meeting the requirements for real-time inference inside a model-predictive-control-style plasma control loop.

Sat 19 SeptMachine Learning
The gist
Estimating the state of plasma inside a Tokamak fusion device quickly is important but hard because control systems need very fast data. The authors present a complete process to run a special kind of neural network (called a recurrent probabilistic neural network) on an FPGA chip to do this estimation. They shrink the model and train it with techniques to make it run efficiently in hardware. Their design runs in less than 10 microseconds per step and fits within the hardware limits, making it suitable for use in controlling plasma in real time.
Open → 2609.23141v1