3 papers
eess.IV2026
CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction
Qingyong Zhu, Yumin Tan, Xiang Gu +1
Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction. Representative FU-DG…
physics.plasm-ph2025
Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation
Siqi Ding, Zitong Zhang, Guoyang Shi +7
As artificial intelligence emerges as a transformative enabler for fusion energy commercialization, fast and accurate solvers become increasingly critical. In magnetic confinement…
physics.plasm-ph2025
EFIT-mini: An Embedded, Multi-task Neural Network-driven Equilibrium Inversion Algorithm
Guohui Zheng, Songfen Liu, Huasheng Xie +11
Equilibrium reconstruction, which infers internal magnetic fields, plasmas current, and pressure distributions in tokamaks using diagnostic and coil current data, is crucial for co…