Towards Tokamak operations Conversational AI Interface Using Multimodal Large Language Models
AI-assisted Plasma State Monitoring for Control and Disruption-free Operations in Tokamaks
Identification and confinement scaling of hybrid scenarios across multiple devices
Machine learning accelerated pedestal MHD stability simulations
Applying AI/ML for NBI ionization and slowing-down simulations using ASCOT/BBNBI
AI-augmented SOL modelling for capturing impact of filaments on transport and PWI in mean field codes simulations.
LIBS data-processing with Deep Neural Networks and Convolutional Neural Networks for chemical composition quantification in the wall of the next step-fusion reactors
AI-assisted Causality Detection and Modelling of Plasma Instabilities for Tokamak Disruption Prediction and Control
Development of Physics Informed Neural Networks (PINNs) for Modelling and Prediction of Data in the Form of Time Series
Deep Learning for Spectrogram Analysis of Reflectometry Data
Testing cutting-edge AI research to increase pattern recognition and image classification in nuclear fusion databases
Surrogate modelling of ray-tracing and radiation transport code for faster real-time plasma profile inference in a magnetic confinement device
Fast inference methods of advanced diagnostics for real-time control
