5 papers
FPGA-Accelerated Real-Time Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference
Abhilasha Dave, Semin Joung, SangKyeun Kim +11
In this work, we demonstrate the deployment of a hardware-accelerated machine learning (ML) inference system integrated into a real-time processing at the DIII-D tokamak fusion rea…
Revisiting confinement scalings and fusion performance with a perspective optimized for extrapolation
Jalal Butt, Geert Verdoolaege, Stanley M. Kaye +1
Recent advances in high-temperature-superconductor technology have made substantially higher toroidal magnetic fields technologically accessible, reopening the design space for com…
FPGA-Accelerated Real-Time Beam Emission Spectroscopy Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference
Abhilasha Dave, James Russell, Mudit Mishra +9
Achieving reliable real-time control of tokamak plasmas is essential for sustaining high-performance operation in next-generation fusion reactors. A major challenge is the accurate…
Enabling Integrated AI Control on DIII-D: A Control System Design with State-of-the-art Experiments
Andrew Rothstein, Hiro Joseph Farre-Kaga, Jalal Butt +7
We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learing (ML)-based predictors and control…
Control of pedestal-top electron density using RMP and gas puff at KSTAR
Minseok Kim, S. K. Kim, A. Rothstein +20
We report the experimental results of controlling the pedestal-top electron density by applying resonant magnetic perturbation with the in-vessel control coils and the main gas puf…