Machine-Learning-Enabled Full-State Reconstruction of Fusion Plasmas from Minimal Sensor Measurements
This paper presents a machine learning model that reconstructs full-domain plasma states from sparse sensor measurements by combining temporal encoding with spatial decoding, offering a robust solution for real-time state estimation and digital twins in fusion reactors where traditional diagnostics and simulations are limited.