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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The central magnetic flux density is usually simulated via finite element methods that require a significant amount of inputs and computation resources. We develop the Gaussian process regression (GPR) model to shed light on the statistical relationship between structural descriptors of the iron yoke and the central magnetic flux density for superconducting solenoids. The model is highly stable and accurate, contributing to fast and robust estimations of the central magnetic flux density.

Machine Learning the Central Magnetic Flux Density of Superconducting Solenoids