Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling

ArXi:2510.00233v2 Announce Type: replace Scientific machine learning has enabled the extraction of physical insights and data-driven modeling of high-dimensional spatiotemporal data, yet achieving physically interpretable latent representations and computationally efficient surrogates remains an open challenge.