Neuronal extraction of information, structures and symmetries in images
![EU Logo](/4977155/original-1639477939.jpg?t=eyJ3aWR0aCI6MjQ2LCJvYmpfaWQiOjQ5NzcxNTV9--fabb5999ab737785eb22fa303066ee8dc1ea69b4)
The NEISS project is funded by the Excellence Research Program of the state of Mecklenburg-Vorpommern with contributions from the European Social Fund (ESF) of the European Union.
![Proof of concept: Deep neural networks can accurately reconstruct plasma parameters (here a proxy for the rotational transform at the edge) from heat load images which will aid machine control.](/4977105/original-1608631796.jpg?t=eyJ3aWR0aCI6MjQ2LCJvYmpfaWQiOjQ5NzcxMDV9--52909e1b0c5319c30ede44dfee83496cf6b5e999)
Max Planck Institute for Plasma Physics is participating in the NEISS project (Neuronal Extraction of Information, Structures and Symmetries in Images) in a research alliance with the University of Rostock. The focus of this interdisciplinary project, which is part of the excellence research program of the state of Mecklenburg-Vorpommern, is the analysis of images using artificial intelligence and machine learning.
One of the things being worked on in the project's plasma physics work package is a control system for Wendelstein 7-X involving measurements of not just one but several diagnostic systems. This includes, for example, images from infrared or X-ray cameras or data supplied by spectroscopic or magnetic diagnostics. Control signals are to be obtained from these in real time and fed back to the machine. Such a real-time control system should guarantee optimal and reliable operation of the machine.