Encoder-Approximator-Decoder

A classic three-stage framework for constructing operator surrogate models, consisting of an encoder (mapping infinite-dimensional inputs to a finite-dimensional latent space), an approximator (processing information in the latent space), and a decoder (mapping latent representations back to the infinite-dimensional output space). Its justification relies on the assumption that data in high-dimensional function spaces lie near a low-dimensional manifold.