Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces

Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces

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Time-consuming numerical simulators for solving groundwater flow tokidoki hello kitty blind box and dissolution models of physico-chemical processes in deep aquifers normally require some of the model inputs to be defined in high-dimensional spaces in order to return realistic results.Sometimes, the outputs of interest are spatial fields leading to high-dimensional output spaces.Although Gaussian process emulation has been satisfactorily used for computing faithful and inexpensive approximations of complex simulators, these have been mostly applied to problems defined in low-dimensional input spaces.In this paper, we propose a method for simultaneously reducing the dimensionality of very high-dimensional input and output spaces in Gaussian process emulators for stochastic partial differential equation models while retaining the qualitative features of the original models.This allows us to build a surrogate model for the prediction of spatial read more fields in such time-consuming simulators.

We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.