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AI Approach Points to Bright Future for Fusion Energy

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.”

Princeton Team using Deep Learning to develop Fusion Energy

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.”

Marina Becoulet from CEA to Keynote PASC18

Today the PASC18 conference announced that Marina Becoulet from CEA will be one of its keynote speakers. “The main goal of the International Thermonuclear Experimental Reactor (ITER) project is the demonstration of the feasibility of future clean energy sources based on nuclear fusion in magnetically confined plasma. In the era of ITER construction, fusion plasma theory and modeling provide not only a deep understanding of a specific phenomenon, but moreover, modeling-based design is critical for ensuring active plasma control.”

Interview: The Computational Challenges of Fusion Energy

In this video from PASC17, Yasuhiro Idomura from the Japan Atomic Energy Agency and Laurent Villard from EPFL discuss the computational challenges of developing Fusion reactors. “Numerical plasma physics models are used to improve our understanding of transport, instability growth and other poorly understood phenomena encountered in the experimental devices edging toward viable fusion energy. Since computational expense imposes a major limitation on accurate physical modeling, computational resources must be used as efficiently as possible.”

Cobham Opera Simulation Software Moves Tokamak Closer to Fusion Energy

The Cobham Technical Services Opera software is helping Tokamak Energy to reduce the very high costs associated with prototyping a new fusion power plant concept,” said Paul Noonan, R&D Projects Director for ST40. “After we have built our new prototype, we hope to have assembled some profoundly exciting experimental and theoretical evidence of the viability of producing fusion power from compact, high field, spherical tokamaks.”