AI RESEARCH
A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
arXiv CS.LG
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ArXi:2605.11759v1 Announce Type: cross Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeling, and systematic design-space exploration. Parametric Model Embedding (PME) addresses this issue by constructing reduced variables from geometric information while preserving an explicit backmapping to the original design parameters. However, PME is intrinsically linear and may become inefficient when the sampled design space is governed by nonlinear geometric variability. This paper