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Reviewing the use of Gaussian process metamodel for several applications
A possible complication might arise from the fact that there are two layers of output in a Principal component model.
The first is the principal component score metamodeling error using the usual metric, Q2 and RMSE.
The second is the reconstruction error from using a truncated principal component coupled with the score predicted by the metamodel. The error here will be more intuitive.
Four types of design of computer experiment are considered:
The effects using on using 5 different covariance/correlation functions are investigated for the Sandia Thermal Challenge problem. The covariance function includes:
Considered 4 different replications for the two-dimensional problem: 4, 8, 16, 32
Three different regression formulas for constructing a GP metamodel for the Sandia Thermal problem are considered:
Check if there is differences.
Three transformations are considered:
Don't forget to shift the output because they are not strictly positive.
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