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

Gaussian process regression (GPR) has been a well-known machine learningmethod for various applications such as uncertainty quantifications (UQ).However, GPR is inherently a data-driven method, which requires sufficientlylarge dataset. If appropriate physics constraints (e.g. expressed in partialdifferential equations) can be incorporated, the amount of data can be greatlyreduced and the accuracy further improved. In this work, we propose a hybriddata driven-physics constrained Gaussian process regression framework. Weencode the physics knowledge with Boltzmann-Gibbs distribution and derive ourmodel through maximum likelihood (ML) approach. We apply deep kernel learningmethod. The proposed model learns from both data and physics constraintsthrough the training of a deep neural network, which serves as part of thecovariance function in GPR. The proposed model achieves good results inhigh-dimensional problem, and correctly propagate the uncertainty, with verylimited labelled data provided.

A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification