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 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 caruso milk thistle mostly applied to problems defined in low-dimensional input spaces.

In this paper, we pomyslnaszycie.com 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 fields in such time-consuming simulators.We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.