5 papers
The Fusion Equilibrium Challenge: Inferring Magnetic Geometry Without Magnetic Diagnostics
Tapan Ganatma Nakkina, Matthew Waller, Craig Michoski +6
Next-generation fusion reactor devices such as SPARC, ARC, and CFETR will operate in extreme neutron environments that compromise the magnetic sensors traditionally used to reconst…
Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
Renjith Prasad, Rishabh Sharma, Andrew E. Shao +8
Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, making them both difficult to a…
The Data Fusion Labeler (dFL): Challenges and Solutions to Data Harmonization, Labeling, and Provenance in Fusion Energy
Craig Michoski, Matthew Waller, Brian Sammuli +14
Fusion energy research increasingly depends on the ability to integrate heterogeneous, multimodal datasets from high-resolution diagnostics, control systems, and multiscale simulat…
Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science
Kishansingh Rajput, Malachi Schram, Brian Sammuli +1
Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distributi…
TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion
Yadi Cao, Futian Zhang, Wesley Liu +7
The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations r…