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.
A paired autoencoder framework for inverse problems via bayes risk minimization.SIAM Journal on Scientific Computing, 48(2):C385–C414
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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.