Multifidelity Computer Model Emulation with High-Dimensional Output.
2019
Hurricane-driven
storm surgeis one of the most deadly and costly natural disasters, making precise quantification of the
surgehazard of great importance.
Physics-based
computermodels of
storm surgecan be implemented with a wide range of
fidelitydue to the nature of the system, though the danger posed by
surgemakes greater
fidelityhighly desirable. However, such models and their high-dimensional outputs tend to come at great computational cost, which can make highly detailed studies prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different
fidelitylevels important. We propose a parallel partial autoregressive cokriging model that is able to address these issues. Based upon the data-augmentation technique, model parameters are estimated via Monte Carlo
expectation-maximization algorithmand prediction is made in a computationally efficient way when input designs across different
fidelitylevels are not nested. This methodology is applied to emulate high-
fidelity
storm surgesin Southwestern Florida from two different
surgemodels. R codes are provided to show the numerical demonstration through an R package called ARCokrig.
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