Aug. 31, 2023 — Today, the U.S. Department of Energy (DOE) announced $29 million in funding for seven team awards for research in machine learning, artificial intelligence and data resources for fusion energy sciences. The funding is for projects lasting up to three years, with $11 million in Fiscal Year 2023 dollars and outyear funding contingent on […]

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Feb. 6, 2023 — HPC industry analyst firm Hyperion Research announced that the HPC User Forum will hold a conference on Tuesday and Wednesday, April 18-19, at the Princeton, NJ, Marriott at Forrestal. The conference agenda can be found here, more information and registration can be found here. Hyperion Research will provide an HPC market update […]

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The DOE SC program in Fusion Energy Sciences (FES) has announced its interest in applications in the areas of machine learning (ML), artificial intelligence (AI) and data resources for fusion energy and plasma sciences. The goal of this FOA is to support multi-disciplinary teams aiming to apply advanced and autonomous algorithms to address high-priority research […]

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A team led by C.S. Chang at Princeton Plasma Physics Laboratory (PPPL) has used the Oak Ridge Leadership Computing Facility’s (OLCF’s) 200-petaflop Summit and Argonne Leadership Computing Facility’s (ALCF’s) 11.7-petaflop Theta supercomputers, together with a supervised machine learning program called Eureqa, to find....

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Scientists from Princeton Plasma Physics Laboratory are leading an Aurora ESP project that will leverage AI, deep learning, and exascale computing power to advance fusion energy research. "With a suite of the world’s most powerful path-to-exascale supercomputing resources at their disposal, William Tang and colleagues are developing models of disruption mitigation systems (DMS) to increase warning times and work toward eliminating major interruption of fusion reactions in the production of sustainable clean energy."

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Researchers at the DIII-D National Fusion Facility achieved a scientific first this month when they used machine learning calculations to automatically prevent fusion plasma disruptions in real time, while simultaneously optimizing the plasma for peak performance. The new experiments are the first of what they expect to be a wave of research in which machine learning–augmented controls could broaden the understanding of fusion plasmas. The work may also help deliver reliable, peak performance operation of future fusion reactors.

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Researchers are using Deep Learning techniques on DOE supercomputers to help develop fusion energy. "Unlike classical machine learning methods, FRNN—the first deep learning code applied to disruption prediction—can analyze data with many different variables such as the plasma current, temperature, and density. Using a combination of recurrent neural networks and convolutional neural networks, FRNN observes thousands of experimental runs called “shots,” both those that led to disruptions and those that did not, to determine which factors cause disruptions."

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Over at the NVIDIA Blog, Tonie Hansen writes that Princeton researchers are using deep learning to help establish the feasibility of delivering fusion energy in the foreseeable future. "The Princeton team has scaled up the capabilities of its FRNN software using thousands of GPUs to train deep neural networks. After successfully running on 6,000 Tesla K20 GPUs on Oak Ridge National Laboratory’s Titan supercomputer, FRNN has recently demonstrated the ability to scale to 3,000 NVIDIA Tesla P100 GPUs on Japan’s new TSUBAME-3 supercomputer at the Tokyo Institute of Technology."

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