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Sparsifying to optimize over multiple information sources: an augmented Gaussian process based algorithm
2021
Structural And Multidisciplinary Optimization
AbstractOptimizing a black-box, expensive, and multi-extremal function, given multiple approximations, is a challenging task known as multi-information source optimization (MISO), where each source has a different cost and the level of approximation (aka fidelity) of each source can change over the search space. While most of the current approaches fuse the Gaussian processes (GPs) modelling each source, we propose to use GP sparsification to select only "reliable" function evaluations
doi:10.1007/s00158-021-02882-7
fatcat:mr2cfcz4h5cgthohk4o6hwojsm