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Modelling burning thermonuclear plasma

Authors: S. J. Rose; P. W. Hatfield; R. H. H. Scott;

Modelling burning thermonuclear plasma

Abstract

Considerable progress towards the achievement of thermonuclear burn using inertial confinement fusion has been achieved at the National Ignition Facility in the USA in the last few years. Other drivers, such as the Z-machine at Sandia, are also making progress towards this goal. A burning thermonuclear plasma would provide a unique and extreme plasma environment; in this paper we discuss (a) different theoretical challenges involved in modelling burning plasmas not currently considered, (b) the use of novel machine learning-based methods that might help large facilities reach ignition, and (c) the connections that a burning plasma might have to fundamental physics, including quantum electrodynamics studies, and the replication and exploration of conditions that last occurred in the first few minutes after the Big Bang. This article is part of a discussion meeting issue ‘Prospects for high gain inertial fusion energy (part 1)’.

Country
United Kingdom
Keywords

fusion, machine learning, General Science & Technology, 530, plasma

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Top 10%
Green
bronze