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Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
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Materials in Nuclear Energy Systems (MiNES 2023)
December 10–14, 2023
New Orleans, LA|New Orleans Marriott
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
U.K., South Korea form new clean energy partnership
The United Kingdom has announced a new partnership with South Korea to accelerate the clean energy transition by strengthening cooperation on low-carbon technologies, domestic climate policies, and civil nuclear energy.
Signed November 22 in London by British energy security and net zero secretary Claire Coutinho and South Korean minister for trade, industry, and energy Moon Kyu Bang, the partnership promotes U.K.-South Korean business collaboration, addressing barriers to trade and encouraging mutual development of the two nations’ energy sectors.
Diogo R. Ferreira, Pedro J. Carvalho, Horácio Fernandes, JET Contributors
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 47-56
Technical Paper | doi.org/10.1080/15361055.2017.1390386
Articles are hosted by Taylor and Francis Online.
Plasma tomography consists of reconstructing a two-dimensional radiation profile of a poloidal cross section of a fusion device based on line-integrated measurements along several lines of sight. The reconstruction process is computationally intensive, and in practice, only a few reconstructions are usually computed per pulse. In this work, we trained a deep neural network based on a large collection of sample tomograms that have been produced at JET over several years. Once trained, the network is able to reproduce those results with high accuracy. More importantly, it can compute all the tomographic reconstructions for a given pulse in just a few seconds. This makes it possible to visualize several phenomena—such as plasma heating, disruptions, and impurity transport—over the course of the entire pulse.