Automatic Identification of MHD Modes in Magnetic Fluctuations Spectrograms using Deep Learning Techniques

11/25/2020

∙

The control and mitigation of MHD oscillations modes is an open problem in fusion science because they can contribute to the outward particle/energy flux and can drive the device away from ignition conditions. It is then of general interest to extract the mode information from large experimental databases in a fast and reliable way. We present a software tool based on Deep Learning that can identify these oscillations modes taking Mirnov coil spectrograms as input data. It uses Convolutional Neural Networks that we trained with manually annotated spectrograms from the TJ-II stellarator database. We have tested several detector architectures, resultingin a detector AUC score of 0.99 on the test set. Finally, it is applied to find MHD modes in our spectrograms to show how this new software tool can be used to mine other databases.

READ FULL TEXT

Automatic Identification of MHD Modes in Magnetic Fluctuations Spectrograms using Deep Learning Techniques

Mode hunting through active information

Emergence of hierarchical modes from deep learning

MinCall - MinION end2end convolutional deep learning basecaller

Jet-Images -- Deep Learning Edition

End-to-end Recording Device Identification Based on Deep Representation Learning

Baryon acoustic oscillations reconstruction using convolutional neural networks

The Underlying Correlated Dynamics in Neural Training

Automatic Identification of MHD Modes in Magnetic Fluctuations Spectrograms using Deep Learning Techniques

Related Research

Mode hunting through active information

Emergence of hierarchical modes from deep learning

MinCall - MinION end2end convolutional deep learning basecaller

Jet-Images -- Deep Learning Edition

End-to-end Recording Device Identification Based on Deep Representation Learning

Baryon acoustic oscillations reconstruction using convolutional neural networks

The Underlying Correlated Dynamics in Neural Training