2 citations · 5 across the 7 of their papers we have counts for
11 papers
Tokamak-SI: Interpreting Tokamak Discharges with Labeled Catalogs
Nathaniel Chen, Alvin Garcia, Ricardo Shousha +3
Finding which phenomena occur in a tokamak discharge, and when, is often a manual task. Previous efforts at labeling events have been task-specific, each with its own corpus of dat…
IGNITE Tokamak World Model Architecture
Peter Steiner, Azarakhsh Jalalvand, Nathaniel Chen +4
We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data…
Is Your AI Fast Enough to Run a Fusion Reactor?
Nathaniel Chen, Andrew Rothstein, Ricardo Shousha +4
Machine learning models are increasingly used in feedback control loops for nuclear fusion, where inference speed and predictable timing are critical. We summarize lessons from mod…
Real-time feedback control of ELM frequency using divertor gas puffing and its effects on tungsten-induced radiation and plasma performance in KSTAR
Minseok Kim, Young-Ho Lee, SangKyeun Kim +10
The edge-localized mode (ELM) frequency () was successfully controlled in real time on KSTAR using a proportional-integral (PI) feedback controller, employing a $…
Offline Reinforcement Learning for Rotation Profile Control in Tokamaks
Rohit Sonker, Hiro Josep Farre Kaga, Jiayu Chen +5
Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved. One such challen…
Photodiode based multi-modal diagnostic for low-energy neutral beam injection in the LTX- spherical tokamak
A. Maan, Tosh Le, D. P. Boyle +8
We present a compact photodiode-based diagnostic array developed to study low-energy neutral beam injection in the LTX- spherical tokamak. The in-vacuum diagnostic combines filt…
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…
TorbeamNN: Machine learning based steering of ECH mirrors on KSTAR
Andrew Rothstein, Minseok Kim, Minho Woo +12
We have developed TorbeamNN: a machine learning surrogate model for the TORBEAM ray tracing code to predict electron cyclotron heating and current drive locations in tokamak plasma…
Interpreting AI for Fusion: an application to Plasma Profile Analysis for Tearing Mode Stability
Hiro J Farre-Kaga, Andrew Rothstein, Rohit Sonker +6
AI models have demonstrated strong predictive capabilities for various tokamak instabilities--including tearing modes (TM), ELMs, and disruptive event--but their opaque nature rais…
Highest Fusion Performance without Harmful Edge Energy Bursts in Tokamak
SangKyeun Kim, Ricardo Shousha, SeongMoo Yang +20
The path of tokamak fusion and ITER is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from th…