Artificial intelligence helps accelerate progress toward efficient fusion reactions | Princeton University

Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Timely prediction of disruptions, the sudden loss of control of the hot, charged plasma that fuels the reactions, will be vital to triggering steps to avoid or mitigate such large-scale events.

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"Today, researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability" argue John Greenwald, Science Editor.

Image of plasma disruption in experiment on JET, left, and disruption-free experiment on JET, right. Training the FRNN neural network to predict disruptions calls for assigning weights to the data flow along the connections between nodes. Data from new experiments is then put through the network, which predicts “disruption” or “non-disruption.” The ultimate goal is at least 95 percent correct predictions of disruption events.
Photo: courtesy of Eliot Feibush

Photo: William Tang

Researchers led by William Tang, a PPPL physicist and a lecturer with the rank of professor in astrophysical sciences at Princeton, are developing the code for predictions for ITER, the international experiment under construction in France to demonstrate the practicality of fusion energy. 

Form of ‘deep learning’ 
The new predictive software, called the Fusion Recurrent Neural Network (FRNN) code, is a form of “deep learning” — a newer and more powerful version of modern machine learning software, an application of artificial intelligence. “Deep learning represents an exciting new avenue toward the prediction of disruptions,” Tang said. “This capability can now handle multi-dimensional data.”

FRNN is a deep-learning architecture that has proven to be the best way to analyze sequential data with long-range patterns. Members of the PPPL and Princeton machine-learning team are the first to systematically apply a deep learning approach to the problem of disruption forecasting in tokamak fusion plasmas.

Chief architect of FRNN is Julian Kates-Harbeck, a graduate student at Harvard University and a DOE-Office of Science Computational Science Graduate Fellow. Drawing upon expertise gained while earning a master’s degree in computer science at Stanford University, he has led the building of the FRNN software...

Princeton’s Tiger cluster 
Princeton University’s Tiger cluster of modern GPUs was the first to conduct deep learning tests, using FRNN to demonstrate the improved ability to predict fusion disruptions. The code has since run on Titan and other leading supercomputing GPU clusters in the United States, Europe and Asia, and has continued to show excellent scaling with the number of GPUs engaged.

The researchers seek to demonstrate that this powerful predictive software can run on tokamaks around the world and eventually on ITER.

Also planned is enhancement of the speed of disruption analysis for the increasing problem sizes associated with the larger data sets prior to the onset of a disruptive event.
Support for this project has primarily come to date from the Laboratory Directed Research and Development funds that PPPL provides.

PPPL, on Princeton University’s Forrestal Campus in Plainsboro, New Jersey, is devoted to creating new knowledge about the physics of plasmas — ultra-hot, charged gases — and to developing practical solutions for the creation of fusion energy. PPPL is managed by Princeton for the U.S. Department of Energy’s Office of Science, which is the largest single supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time.
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Source: Princeton University