activity
20192026
most citedStochastic fluctuation and transport of tokamak edge plasmas with the resonant magnetic perturbation field

11 citations · 45 across the 14 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

physics.plasm-ph2025

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…

physics.plasm-ph2025

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…

physics.plasm-ph2025★ 1 cited

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…

cs.LG2025★ 2 cited

Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks

Nathaniel Chen, Cheolsik Byun, Azarakash Jalalvand +7

While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. T…

physics.plasm-ph2025★ 2 cited

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…

physics.plasm-ph2025★ 2 cited

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…

physics.plasm-ph2025★ 7 cited

Gyrokinetic simulations of the effects of magnetic islands on microturbulence in KSTAR

Xishuo Wei, Javier H Nicolau, Gyungjin Choi +8

Gyrokinetic simulations are utilized to study effects of magnetic islands on the ion temperature gradient (ITG) turbulence in the KSTAR tokamak with resonant magnetic perturbations…