2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
Bayesian Active Learning of Ion Loss-Cone Boundaries in Tokamaks
Omar E. Lopez, Minglei Yang, Mark Cianciosa +1
Loss-cone boundary determination in tokamaks is cast as an active learning problem in which a Bayesian logistic regression model with radial basis function features acts as the lea…
MPEX AI Digital Twins Milestone Report
Gary Staebler, Rhea Barnett, Mark Cianciosa +13
This is the six month progress report to Fusion Energy Science (FES) and the American Science Cloud (AmSC) on the MPEX AI Digtial Twins project that was started in October 2025. Th…
MPEX AI Digital Twins
Gary Staebler, Rhea Barnett, Mark Cianciosa +10
Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analy…
A machine learning framework for developing quasilinear saturation rules of turbulent transport from linear gyrokinetic data
Preeti Sar, Sebastian De Pascuale, Harry Dudding +1
A new neural network model for a quasilinear saturation rule has been developed to map linear gyrokinetic data to nonlinear saturated potential magnitudes to predict the total ener…
Microtearding mode study in NSTX using machine learning enhanced reduced model
Max T. Curie, Joel Larakers, Jason Parisi +18
This article presents a survey of NSTX cases to study the microtearing mode (MTM) stabilities using the newly developed global reduced model for Slab-Like Microtearing modes (SLiM)…