Author

Ismaël Zighed

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#machine learning Feb 2026

Efficient adaptation of ROMs for unsteady flows using data assimilation

It is shown that the dominant source of error in out-of-sample forecasts stems from distortions of the latent manifold rather than changes in the latent dynamics, allowing for a lightweight, computationally efficient adaptation procedure with very sparse fine-tuning data.

Ismaël Zighed, Andrea Nóvoa, Luca Magri et al. · Computers & Fluids · 0 citations

Preprint Aug 2026

Modelisation of chaotic systems with a latent Stochastic Differential Equation

This work introduces a probabilistic, non-intrusive reduced-order model (ROM) for chaotic dynamical systems, arguing that projecting high-dimensional nonlinear dynamics onto a low-dimensional manifold introduces irreducible uncertainty, compounded by the chaotic attractors and multi-admissible futures inherent to turbulent flows.

Ismaël Zighed, Nicolas Thome, Patrick Gallinari et al. · 0 citations