Emulating dynamic non-linear simulators using Gaussian processes
dc.contributor.author | Mohammadi, H | |
dc.contributor.author | Challenor, P | |
dc.contributor.author | Goodfellow, M | |
dc.date.accessioned | 2019-07-04T13:21:44Z | |
dc.date.issued | 2018-05-21 | |
dc.description.abstract | The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution of some real-world phenomenon over time, for example models of the climate or the functioning of the human brain. The models we are interested in are highly non-linear and exhibit tipping points, bifurcations and chaotic behaviour. However, each simulation run could be too time-consuming to perform analyses that require many runs, including quantifying the variation in model output with respect to changes in the inputs. Therefore, Gaussian process emulators are used to approximate the output of the code. To do this, the flow map of the system under study is emulated over a short time period. Then, it is used in an iterative way to predict the whole time series. A number of ways are proposed to take into account the uncertainty of inputs to the emulators, after fixed initial conditions, and the correlation between them through the time series. The methodology is illustrated with two examples: the highly non-linear dynamical systems described by the Lorenz and van der Pol equations. In both cases, the predictive performance is relatively high and the measure of uncertainty provided by the method reflects the extent of predictability in each system. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Wellcome Trust | en_GB |
dc.identifier.citation | Vol. 139, pp. 178-196 | en_GB |
dc.identifier.doi | 10.1016/j.csda.2019.05.006 | |
dc.identifier.grantnumber | EP/N014391/1 | en_GB |
dc.identifier.grantnumber | EP/P021417/1 | en_GB |
dc.identifier.grantnumber | WT105618MA | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/37847 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier for International Association for Statistical Computing | en_GB |
dc.rights | © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) | en_GB |
dc.subject | Dynamic emulators | en_GB |
dc.subject | Gaussian Processes | en_GB |
dc.subject | Uncertainty Propagation | en_GB |
dc.subject | Lorenz | en_GB |
dc.subject | Van de Pol | en_GB |
dc.title | Emulating dynamic non-linear simulators using Gaussian processes | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-07-04T13:21:44Z | |
dc.description | This is the final version. Available on open access from Elsevier via the DOI in this record | en_GB |
dc.identifier.eissn | 1872-7352 | |
dc.identifier.journal | Computational Statistics and Data Analysis | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-05-13 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-05-13 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-07-04T13:14:13Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-07-04T13:21:50Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)