Author

Constantinos I. Siettos

2 papers indexed here

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Preprint Sep 2026

Autoencoders vs. Numerical Analysis--Informed Manifold Learning for Navier--Stokes Flows

Autoencoders (AEs) have become a dominant approach to nonlinear latent-space construction in data-driven reduced-order modelling (ROM), with their decoders lifting latent representations back to the ambient state space. Their prominence, however, has overshadowed an established alternative: manifold-learning methods gr...

A. Della Pia, Lucia Russo, I. Kevrekidis et al. · 0 citations

Open access Aug 2026

Going with the flow to solve for symmetry-driven PDE dynamics with physics-informed neural networks

This work addresses a computational framework that integrates symmetry reduction into Physics-Informed Neural Networks (PINNs) for analysing symmetry-driven dynamics in nonlinear partial differential equations (PDEs), enabling accurate forward simulation and parameter inference without front tracking or mesh adaptation...

M. Kavousanakis, Gianluca Fabiani, A. Georgiou et al. · Nature Communications · 2 citations