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Multilevel Oblique Lumbar Interbody Fusion in Degenerative Lumbar Disc Disease with Instability

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Predicting disruptive instabilities in controlled fusion plasmas through deep learning.

Julian Kates-Harbeck^1,2,3, Alexey Svyatkovskiy^4,5, William Tang^6,4

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

|April 19, 2019

Summary

A new deep learning method accurately forecasts disruptions in nuclear fusion reactors, even on different machines. This advance is crucial for the reliable operation of future fusion energy power plants.

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Area of Science:

Nuclear Fusion Energy
Plasma Physics
Machine Learning Applications

Background:

Magnetic-confinement tokamak reactors promise sustainable, clean energy.
Plasma disruptions are a major challenge, halting power production and damaging components.
Accurate disruption prediction is critical for large-scale projects like ITER.

Purpose of the Study:

To develop an advanced deep learning method for forecasting disruptions in tokamak reactors.
To improve upon existing first-principles and classical machine learning approaches.
To enable reliable disruption prediction across different fusion machines.

Main Methods:

Utilized a deep learning approach trained on high-dimensional experimental data.
Leveraged supercomputing resources for enhanced accuracy and speed.
Trained models on data from DIII-D and Joint European Torus (JET) tokamaks.

Main Results:

The deep learning method demonstrated reliable disruption prediction capabilities.
Achieved successful cross-machine prediction, a key requirement for future reactors.
Enabled prediction with long warning times, facilitating active reactor control.

Conclusions:

Deep learning offers a powerful tool for advancing fusion energy science.
The developed method significantly enhances disruption forecasting for tokamaks.
This approach has broader implications for predicting complex physical systems.