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Found 2 papers, 1 papers with code
Grassmannian diffusion maps based surrogate modeling via geometric harmonics
Using this representation, geometric harmonics, an out-of-sample function extension technique, is employed to create a global map from the space of input parameters to a Grassmannian diffusion manifold.
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner.
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