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Active subspace methods have previously been developed only for single (scalar) model outputs, however.
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This study uses the active subspace method to identify low-dimensional subspace of the input space, within which response surfaces can be built.
In present paper, a supervised dimension reduction methodology named Active Subspace Method (Constantine, 2015) is introduced to deal with the high dimension problem of structural reliability.
The results show that the Active Subspace Kriging (ASK) method significantly improved the result of extreme-value reliability analysis for stochastic nonlinear structures, in which high dimensional randomness problem is involved.
The score is determined by the weighted average of the chemical activity values in an active subspace A and its neighboring subspaces.
Nonparametric methods include beamforming and subspace methods.
A wide range of physical responses exhibit a special structure known as an active subspace (AS).
The active subspace effectively reduces the dimension and measures the contributions of input uncertainties.
The subspace methods estimate the signal-plus-interference subspace to reduce mismatch.
Subspace methods rely on the orthogonality property between the signal and noise subspaces.
The subspace methods can be classified into two major categories: incoherent signal-subspace method (ISSM [2] and coherent signal-subspace method (CSSM [3 6].
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