English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fusion-graded plasmas are one of the physically complex systems, resulting incontinuous establishment of plasma theories for unclarified physical phenomenain order to thoroughly control nuclear fusion reactors. Deep learning has drawnvast attention to this field of controlled fusion plasma to link physicalphenomena with control-relevant parameters without a deepened understandingabout plasma theories. Albeit, quantifying the uncertainty of deep learningmodels has been constantly requested due to their fundamental shortage ofphysical understanding. Thus, a concept of a reliable deep learning model to beable to present their probability distributions is raised as well as a methodto inculcate physical theories in the model is also concerned.

Bayesian neural network for plasma equilibria in the Korea Superconducting Tokamak Advanced Research