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

We present an integrated approach for the use of simulated data from fullorder discretization as well as projection-based Reduced Basis reduced ordermodels for the training of machine learning approaches, in particular KernelMethods, in order to achieve fast, reliable predictive models for the chemicalconversion rate in reactive flows with varying transport regimes.

A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows