Computer model calibration with large nonstationary spatial outputs: application to the calibration of a climate model
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About this item
| Description: |
Guillas, S
Friday 13th April 2018 - 10:00 to 10:30 |
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| Created: | 2018-04-13 15:40 |
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| Collection: | Uncertainty quantification for complex systems: theory and methodologies |
| Publisher: | Isaac Newton Institute |
| Copyright: | Guillas, S |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Bayesian calibration of computer models tunes unknown input parameters by comparing outputs to observations. For model outputs distributed over space, this becomes computationally expensive due to the output size. To overcome this challenge, we employ a basis representations of the model outputs and observations: we match these decompositions to efficiently carry out the calibration. In a second step, we incorporate the nonstationary behavior, in terms of spatial variations of both variance and correlations, into the calibration. We insert two INLA-SPDE parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach. |
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