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 discontinuities. We demonstrate the effectiveness of the new technology using standard benchmark problems designed to resemble the outputs of complicated high- and low-fidelity codes.
Cite
@article{arxiv.1604.07484,
title = {Deep Multi-fidelity Gaussian Processes},
author = {Maziar Raissi and George Karniadakis},
journal= {arXiv preprint arXiv:1604.07484},
year = {2016}
}