2 citations · 2 across the 2 of their papers we have counts for
5 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, Cheolsik Byun, Andrew Rothstein +7
In magnetic confinement fusion, compact machine learning algorithms are often integrated into the control loop on CPU-based custom hardware stacks. Deterministic implementations of…
Towards Large-Scale Heterogeneous Data Organization for Scientific Foundation Models: A Nuclear Fusion Case Study
Nathaniel Chen, Kouroche Bouchiat, Peter Steiner +3
Training effective foundation models requires massive and organized datasets, yet scientific domains such as nuclear fusion present unique challenges due to largely heterogeneous a…
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…