# Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model

arXiv: https://arxiv.org/abs/1604.04478 · HTML: https://mlsanity.com/paper/1604.04478/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors](https://mlsanity.com/paper/2201.06463/) (2022)
- [Computationally-Efficient Climate Predictions using Multi-Fidelity Surrogate Modelling](https://mlsanity.com/paper/2109.07468/) (2021)

## Abstract (arXiv)

Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome this challenge, we employ a basis representation of the model outputs and observations: we match these decompositions to carry out the calibration efficiently. In the second step, we incorporate the non-stationary behaviour, in terms of spatial variations of both variance and correlations, in the calibration. We insert two integrated nested Laplace approximation-stochastic partial differential equation parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach.