Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of parameter-dependent high-dimensional dynamical 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, more recently, on neural network architectures such as autoencoders (AEs). In the case that the ROM is constructed by the POD, one has approximation guaranteed based based on the singular values of the pro...
| Authors | Nicolò Botteghi, Silke Glas, Christoph Brune |
|---|---|
| Primary category | cs.LG |
| Published | 13 March 2026 |
| Last updated at source | 15 September 2026 |
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