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.
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil.Engineering with Computers, 39(3):2239– 2255
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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.