Multi-fidelity modelling via recursive co-kriging and Gaussian-Markov random fields.

Multi-fidelity optimization via surrogate modelling

This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of co-kriging. An exchange algorithm is used to choose which points of the search space to sample within each level of analysis. The derivation of the co-kriging equations is presented in an i...

Deep Multi-fidelity Gaussian Processes

We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O’Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and deep neural networks enables us to construct a method that is immune to disco...

Multi-fidelity Gaussian process regression for prediction of random fields

Article history: Received 17 July 2016 Received in revised form 10 November 2016 Accepted 23 January 2017 Available online xxxx

Fast kriging of large data sets with Gaussian Markov random fields

Spatial data sets are analysed in many scientific disciplines. Kriging, i.e. minimum mean squared error linear prediction, is probably the most widely used method of spatial prediction. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field...

Modelling Gaussian Fields and Geostatistical Data Using Gaussian Markov Random Fields