AI RESEARCH
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
arXiv CS.LG
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ArXi:2603.13496v1 Announce Type: new Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of high-dimensional, parametric systems is crucial in many applications in engineering and applied sciences. A popular class of projection-based ROMs projects the high-dimensional full-order model (FOM) dynamics onto a low-dimensional manifold. These projection-based ROMs approaches often rely on classical model reduction techniques such as proper orthogonal decomposition (POD) or, recently, on neural network architectures such as autoencoders (AEs.