Multilevel Emulation for Stochastic Computer Models with an Application to Large Offshore Windfarms
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Increasingly, stochastic computer models are being used in science and engineering to predict and understand complex phenomena. Despite the power of modern computing, these simulators are often too computationally costly to be of practical use due to their complexity. Hence the emulation of stochastic computer models is a problem of increasing interest. Many stochastic computer models can be run at different levels of complexity, which incurs a trade-off with simulation accuracy. More complex simulations are more expensive to run, but will often be correlated with less complex but cheaper to run versions. We present a heteroscedastic Gaussian process approach to emulation of stochastic simulators which utilises cheap approximations to a stochastic simulator, motivated by a stochastic reliability and maintenance model of a large offshore windfarm. The performance of our proposed methodology is demonstrated on two synthetic examples (a simple, tractable example and a predator-prey model) before being applied to the stochastic windfarm simulator.
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