AI can Accelerate Safer Fusion Energy

Thursday, April 18, 2019 @ 04:04 PM gHale

Depiction of fusion research on a doughnut-shaped tokamak enhanced by artificial intelligence.
Source: Eliot Feibush/PPPL and Julian Kates-Harbeck/Harvard University

Artificial intelligence (AI) could now speed the development of safe, clean and virtually limitless fusion energy for generating electricity.

For the first time a team of scientists from the Department of Energy’s (DoE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are working with a Harvard graduate to apply deep learning — a powerful new version of the machine learning form of AI — to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks that house the reactions.

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“This research opens a promising new chapter in the effort to bring unlimited energy to Earth,” Steve Cowley, director of PPPL, said of the findings. “Artificial intelligence is exploding across the sciences and now it’s beginning to contribute to the worldwide quest for fusion power.”

Fusion, which drives the sun and stars, is the fusing of light elements in the form of plasma — the hot, charged state of matter composed of free electrons and atomic nuclei — that generates energy. Scientists are seeking to replicate fusion on Earth for an abundant supply of power for the production of electricity.

Crucial to demonstrating the ability of deep learning to forecast disruptions — the sudden loss of confinement of plasma particles and energy — has been access to huge databases provided by two major fusion facilities: The DIII-D National Fusion Facility that General Atomics operates for the DoE in California, the largest facility in the United States, and the Joint European Torus (JET) in the United Kingdom, the largest facility in the world, which is managed by EUROfusion, the European Consortium for the Development of Fusion Energy. Support from scientists at JET and DIII-D has been essential for this work.

The vast databases have enabled reliable predictions of disruptions on tokamaks other than those on which the system was trained — in this case from the smaller DIII-D to the larger JET. The achievement bodes well for the prediction of disruptions on ITER, a far larger and more powerful tokamak that will have to apply capabilities learned on today’s fusion facilities. The deep learning code, called the Fusion Recurrent Neural Network (FRNN), also opens possible pathways for controlling as well as predicting disruptions.

“Artificial intelligence is the most intriguing area of scientific growth right now, and to marry it to fusion science is very exciting,” said Bill Tang, a principal research physicist at PPPL, coauthor of the paper and lecturer with the rank and title of professor in the Princeton University Department of Astrophysical Sciences who supervises the AI project. “We’ve accelerated the ability to predict with high accuracy the most dangerous challenge to clean fusion energy.”

Unlike traditional software, which carries out prescribed instructions, deep learning learns from its mistakes. Accomplishing this seeming magic are neural networks, layers of interconnected nodes — mathematical algorithms — that are “parameterized,” or weighted by the program to shape the desired output. For any given input the nodes seek to produce a specified output, such as correct identification of a face or accurate forecasts of a disruption. Training kicks in when a node fails to achieve this task: The weights automatically adjust themselves for fresh data until the correct output is obtained.

A key feature of deep learning is its ability to capture high-dimensional rather than one-dimensional data. For example, while non-deep learning software might consider the temperature of a plasma at a single point in time, the FRNN considers profiles of the temperature developing in time and space. “The ability of deep learning methods to learn from such complex data make them an ideal candidate for the task of disruption prediction,” said collaborator Julian Kates-Harbeck, a physics graduate student at Harvard University and a DoE-Office of Science Computational Science Graduate Fellow who was lead author of the paper and chief architect of the code.

Training and running neural networks relies on graphics processing units (GPUs), computer chips first designed to render 3D images. Such chips are ideally suited for running deep learning applications and are widely used by companies to produce AI capabilities such as understanding spoken language and observing road conditions by self-driving cars.

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