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/

Zendy Plus

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

Zendy Tools

Zendy Open

In many applications, uncertainty in problem data leads to the need fornumerous computationally expensive simulations. This report addresses thischallenge by developing a penalty-based ensemble algorithm. Building upon Jiangand Layton's work on ensemble algorithms that use a shared coefficient matrix,this report introduces the combination of penalty methods to enhance itscapabilities. Penalty methods uncouple velocity and pressure by relaxing theincompressibility condition. Eliminating the pressure results in a system thatrequires less memory. The reduction in memory allows for larger ensemble sizes,which give more information about the flow and can be used to extend thepredictability horizon.

Penalty Ensembles for Navier-Stokes with Random Initial Conditions and Forcing