NLPME achieves lower reconstruction error with fewer latent variables than linear PME on a 32-parameter underwater glider shape while retaining explicit backmapping to design parameters.
Modeling and optimization with gaussian processes in reduced eigenbases.Structural and Multidisciplinary Optimization, 61(6):2343–2361
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A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
NLPME achieves lower reconstruction error with fewer latent variables than linear PME on a 32-parameter underwater glider shape while retaining explicit backmapping to design parameters.