A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas.

ABSTRACT: Experimental observations assisted by 2-D imaging diagnostics on the KSTAR tokamak show that a solitary perturbation (SP) emerges prior to a boundary burst of magnetized toroidal plasmas, which puts forward SP as a potential candidate for the burst trigger. We have constructed a machine learning (ML) model based on a convolutional deep neural network architecture for a statistical study to identify the SP as a boundary burst trigger. The ML model takes sequential signals detected from 19 toroidal Mirnov coils as input and predicts whether each temporal frame corresponds to an SP. We trained the network in a supervised manner on a training set consisting of real signals with manually annotated SP locations and synthetic burst signals. The trained model achieves high performances in various

SUBMITTER: Lee JE 

PROVIDER: S-EPMC7878480 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature