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The method is formulated in the original variable space.
To achieve the multi-resolution strategy, design optimization is formulated in a wavelet-based variable space, not in a direct density variable space.
In exploring a new variable space, process improvements of more than 100% were generally achieved.
As a result, the variable space can be subdivided into subspaces with reduced dimensionality.
The points and weights are predetermined in the independent standard normal variable space.
The sample points in basic variable space are then obtained by various transformations.
PCA [16] is applied for reduction of the descriptor space (variable space).
Therefore, alternative approach is to model process using limited experimental points representing all variable space.
These algorithms are based partly on a random exploration of the variable space.
The PCs obtained from PCA can be defined as variance-scaled vectors in the variable space.
Defining an environmental contour means enclosing a region in the variable space which corresponds to a certain return period.
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