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Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces
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Crevillén-García, D. (2018) Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces. Royal Society Open Science, 5 (4). 171933. doi:10.1098/rsos.171933 ISSN 2054-5703.
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WRAP-surrogate-modelling-prediction-spatial-fields-based-simultaneous-dimensionality-reduction-high-dimensional-Crevillen-Garcia-2018.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1338Kb) | Preview |
Official URL: https://doi.org/10.1098/rsos.171933
Abstract
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 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 fields in such time-consuming simulators. We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QD Chemistry |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Chemistry, Physical and theoretical -- Mathematical models, Gaussian processes, Stochastic partial differential equations | ||||||
| Journal or Publication Title: | Royal Society Open Science | ||||||
| Publisher: | The Royal Society Publishing | ||||||
| ISSN: | 2054-5703 | ||||||
| Official Date: | 25 April 2018 | ||||||
| Dates: |
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| Volume: | 5 | ||||||
| Number: | 4 | ||||||
| Article Number: | 171933 | ||||||
| DOI: | 10.1098/rsos.171933 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 5 June 2018 | ||||||
| Date of first compliant Open Access: | 5 June 2018 | ||||||
| RIOXX Funder/Project Grant: |
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