For stable and efficient fusion energy production using a tokamak reactor, it is essential to
maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is …

Reconstructing the past of observed fluids has been known as an ill-posed problem due to
both numerical and physical challenges, especially when observations are distorted by …

Kinetic equilibrium reconstructions make use of profile information such as particle density
and temperature measurements in addition to magnetics data to compute a self-consistent …

We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium
FIT) that integrates probabilistic and physics-informed methodologies to overcome typical …

We propose an outlier-resilient Gaussian process regression (GPR) model supported by
support vector machine regression (SVMR) for kinetic profile inference. GPR, being a non …

For decades, plasma transport simulations in tokamaks have used the finite difference
method (FDM), a relatively simple scheme to solve the transport equations, a coupled set of …

With the emergence of high-repetition-rate two-dimensional Thomson scattering (TS)
measurements, improving spectral data analysis is a key area of interest. We present a new …

In this research, we develop a data-driven disruption predictor based on Bayesian deep
probabilistic learning, capable of predicting disruptions and modeling uncertainty in KSTAR …

Disruption phenomenon, an abrupt and uncontrolled termination process induced by
various plasma instabilities, is one of the prominent issues in tokamak plasma. This event …

The modelling and analysis of complex physical systems can be approached through data-
driven and physics-driven methods. To leverage their different advantages, it is necessary to …