Author

S. Glas

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Preprint Sep 2026

Spatio-temporal Latent Denoising Diffusion Probabilistic Models for Reduced-order Modeling of Parametrized Dynamical Systems

This work proposes a non-intrusive MOR method using generative machine learning by means of denoising diffusion probabilistic models (DDPMs) for generating solutions of the dynamical systems under different instances of their parameters to improve quality and temporal coherence.

Michiel Nikken, Nicolò Botteghi, Federico Califano et al. · 0 citations

#machine learning Open access Mar 2026

Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems

An invertible AE architecture, named inv-AE, is proposed that computationally improves upon the stagnation of the reconstruction error typical of traditional AE architectures and can be combined with popular autoencoder-based ROM approaches, e.g., DL-ROM, to improve their accuracy.

Nicolò Botteghi, S. Glas, Christoph Brune · Computer Methods in Applied... · 1 citation