Deep Multi-fidelity Gaussian Processes

Maziar Raissi; George Karniadakis

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arXiv · 1604.07484

Deep Multi-fidelity Gaussian Processes

Maziar Raissi·George Karniadakis

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Abstract

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.

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Maziar Raissi, George Karniadakis. 2016-04-26. Deep Multi-fidelity Gaussian Processes. https://arxiv.org/abs/1604.07484

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