Abstract
We present ContourNet, a deep learning approach to identify salient local isocontours as blobs in large-scale 5D gyrokinetic tokamak simulation data. Blobs—regions of high turbulence that run along the edge wall down toward the diverter and can damage the tokamak—are non-well-defined features but have been empirically localized by isocontours in 2D normalized fluctuating density fields. The key of our study is to train ContourNet to follow the empirical rules to detect blobs over the time-varying simulation data. The architecture of ContourNet is a convolutional neural segmentation network: the inputs are the density field and a rasterized isocontour; the output is a set of isocontour encircling blobs. At the training stage, we feed the network with manually identified isocontours and propagated labels. At the inference stage, we extract isocontours from the segmented blob regions. Results show that our approach can achieve both high accuracy and performance, which enables scientists to understand the blob dynamics influencing the confinement of the plasma.
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Notes
- 1.
A PyTorch implementation can be found at https://github.com/mimre25/ContourNet.
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Acknowledgments
This research was supported in part by the U.S. National Science Foundation through grants IIS-1455886, CNS-1629914, and DUE-1833129, and the U.S. Department of Energy through grant DE-AC02-06CH11357 and the Exascale Computing Project (17-SC-20-SC).
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Imre, M. et al. (2019). ContourNet: Salient Local Contour Identification for Blob Detection in Plasma Fusion Simulation Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_22
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