For separable multi-output GPs, oracle prediction gain and cross-dependence estimability are controlled by heterotopic design geometry, so interleaved and separated zero-overlap designs are not statistically equivalent.
Linear coregionalization model: tools for estimation and choice of cross-variogram matrix.Mathematical Geology, 24(3):269–286
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A review of multi-fidelity surrogates from co-kriging to neural networks for composite mechanics, with applications in prediction, optimization, and workflow integration.
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When is multivariate kriging worthwhile? A design-geometry analysis of heterotopic multi-output Gaussian processes
For separable multi-output GPs, oracle prediction gain and cross-dependence estimability are controlled by heterotopic design geometry, so interleaved and separated zero-overlap designs are not statistically equivalent.
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Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
A review of multi-fidelity surrogates from co-kriging to neural networks for composite mechanics, with applications in prediction, optimization, and workflow integration.