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通过深度学习预测受控聚变等离子体的破坏性不稳定性

Julian Kates-Harbeck1,2,3, Alexey Svyatkovskiy4,5, William Tang6,4

  • 1Department of Physics, Harvard University, Cambridge, MA, USA. jkatesharbeck@g.harvard.edu.

Nature
|April 19, 2019
PubMed
概括

一种新的深度学习方法可以准确预测核聚变反应堆中的故障, 这一进步对于未来核聚变发电厂的可靠运行至关重要.

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Proteins and neurotransmitters in secretory vesicles can be released from a cell upon vesicle docking, priming, and fusion with the plasma membrane. Vesicles are docked and primed in preparation for the quick exocytosis of their contents in response to a stimulus. The fusion process is mainly carried out by a SNAP Receptor or SNARE complex, consisting of synaptobrevin, syntaxin-1, and SNAP-25.
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