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

High accuracy complex computer models, or simulators, require large resourcesin time and memory to produce realistic results. Statistical emulators arecomputationally cheap approximations of such simulators. They can be built toreplace simulators for various purposes, such as the propagation ofuncertainties from inputs to outputs or the calibration of some internalparameters against observations. However, when the input space is of highdimension, the construction of an emulator can become prohibitively expensive.In this paper, we introduce a joint framework merging emulation with dimensionreduction in order to overcome this hurdle. The gradient-based kernel dimensionreduction technique is chosen due to its ability to drastically decreasedimensionality with little loss in information. The Gaussian process emulationtechnique is combined with this dimension reduction approach. Our proposedapproach provides an answer to the dimension reduction issue in emulation for awide range of simulation problems that cannot be tackled using existingmethods. The efficiency and accuracy of the proposed framework is demonstratedtheoretically, and compared with other methods on an elliptic partialdifferential equation (PDE) problem. We finally present a realistic applicationto tsunami modeling. The uncertainties in the bathymetry (seafloor elevation)are modeled as high-dimensional realizations of a spatial process using ageostatistical approach. Our dimension-reduced emulation enables us to computethe impact of these uncertainties on resulting possible tsunami wave heightsnear-shore and on-shore. We observe a significant increase in the spread ofuncertainties in the tsunami heights due to the contribution of the bathymetryuncertainties. These results highlight the need to include the effect ofuncertainties in the bathymetry in tsunami early warnings and risk assessments.

Dimension reduction for Gaussian process emulation: an application to the influence of bathymetry on tsunami heights