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Classical model reduction techniques project the governing equations onto a linear subspace of the original state space. More recent data-driven techniques use neural networks to enable nonlinear projections. While those often enable stronger compression, they may have redundant parameters and lead to suboptimal latent dimensionality. To overcome these issues, we propose a multistep algorithm that
