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

Deep neural operators (DNOs) have been utilized to approximate nonlinearmappings between function spaces. However, DNOs face the challenge of increaseddimensionality and computational cost associated with unaligned observationdata. In this study, we propose a hybrid Decoder-DeepONet operator regressionframework to handle unaligned data effectively. Additionally, we introduce aMulti-Decoder-DeepONet, which utilizes an average field of training data asinput augmentation. The consistencies of the frameworks with the operatorapproximation theory are provided, on the basis of the universal approximationtheorem. Two numerical experiments, Darcy problem and flow-field around anairfoil, are conducted to validate the efficiency and accuracy of the proposedmethods. Results illustrate the advantages of Decoder-DeepONet andMulti-Decoder-DeepONet in handling unaligned observation data and showcasetheir potentials in improving prediction accuracy.

A hybrid Decoder-DeepONet operator regression framework for unaligned observation data