most citedMicrotearding mode study in NSTX using machine learning enhanced reduced model

2 citations · 2 across the 3 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci2026

Peridynamic Thermo-Mechanical Modeling of Cracking in Tungsten under Fusion-Relevant Heat Fluxes

Rinkle Juneja, Pablo Seleson, Samuel Temple Reeve +2

Plasma-facing materials (PFMs) are the primary armor materials in fusion reactors, protecting reactor wall components from extreme heat fluxes, energetic particles, and intense neu…

cs.LG2026

An Automated and Reproducible Workflow for Crack Identification and Damage Assessment of Fusion Materials

Rinkle Juneja, Viktor Reshniak, Richard K. Archibald +4

Post-exposure microscopy is central to qualification of fusion materials. However, manual analysis does not scale to the volume, heterogeneity, and multiresolution character of mod…

physics.plasm-ph2026

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…

physics.plasm-ph2026

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…

physics.plasm-ph2026

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…

physics.plasm-ph2026

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…

physics.plasm-ph2023★ 2 cited

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)…