The Onion architecture, which multiplies backbone features with diagnostic response-matrix encodings and adds a line-integral loss, modestly reduces reconstruction error on synthetic and experimental fusion data.
Applications of Deep Learning to Nuclear Fusion Research
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abstract
Nuclear fusion is the process that powers the sun, and it is one of the best hopes to achieve a virtually unlimited energy source for the future of humanity. However, reproducing sustainable nuclear fusion reactions here on Earth is a tremendous scientific and technical challenge. Special devices -- called tokamaks -- have been built around the world, with JET (Joint European Torus, in the UK) being the largest tokamak currently in operation. Such devices confine matter and heat it up to extremely high temperatures, creating a plasma where fusion reactions begin to occur. JET has over one hundred diagnostic systems to monitor what happens inside the plasma, and each 30-second experiment (or pulse) generates about 50 GB of data. In this work, we show how convolutional neural networks (CNNs) can be used to reconstruct the 2D plasma profile inside the device based on data coming from those diagnostics. We also discuss how recurrent neural networks (RNNs) can be used to predict plasma disruptions, which are one of the major problems affecting tokamaks today. Training of such networks is done on NVIDIA GPUs.
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Physics-Informed Deep Learning Model for Line-integral Diagnostics Across Fusion Devices
The Onion architecture, which multiplies backbone features with diagnostic response-matrix encodings and adds a line-integral loss, modestly reduces reconstruction error on synthetic and experimental fusion data.