The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake flows, this is often achieved through Reduced-Order Models (ROMs) that first compress high-dimensional velocity snapshots into a latent space and then learn a time- stepping predictor for the dynamics in the latent space. Here, we study how the choice of the spatial encoder affects the predictability of the resulting latent coordinates for wake flows under control inputs. Using two actuated 2D wake configurations, a simplified truck wake and the fluidic pinball, we compare Proper Orthogonal Decomposition (POD) against nonlinear Convolutional ...
| Authors | Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila et al. |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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