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News/A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
arxiv
PublishedJuly 30, 2026 at 4:00 AM
▲bullish

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.11759v2 Announce Type: replace-cross Abstract: 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

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Discussion
Mentioned models
03
  • 01
    NLPME
  • 02
    PME
  • 03
    deep autoencoder
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#dimensionality-reduction#parametric-modeling#nonlinear-representation#shape-design

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Mentioned models
03
  • 01
    NLPME
  • 02
    PME
  • 03
    deep autoencoder
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#dimensionality-reduction#parametric-modeling#nonlinear-representation#shape-design

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