works on

From the 2 of 23 linked papers with an AI index.

activity
20242026
collaborators

23 papers

physics.flu-dyn2026

The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows

Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila +1

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 t…

physics.flu-dyn2026

Divide and conquer complex flows. Part I: cluster and manifold-based local analysis

Qihong L. Li-Hu, Guy Y. Cornejo Maceda, Andrea Ianiro +1

The paper introduces a data‑driven framework that uses manifold learning and unsupervised clustering to automatically partition complex fluid‑flow domains into subregions with simi…

physics.flu-dyn2026

Feature-based manifold model of actuated wakes

Alicia Rodríguez-Asensio, Guy Y. Cornejo Maceda, Bernd R. Noack +2

The paper presents a feature‑based reduced‑order model that predicts the transient dynamics of bluff‑body wakes under arbitrary time‑varying actuation, using a manifold of dynamic…

physics.flu-dyn2026

Data-efficient semi-supervised learning for flow estimation using unlabelled probe data

Junwei Chen, Marco Raiola, Stefano Discetti

Estimating time-resolved velocity and pressure fields from Particle Image Velocimetry (PIV) remains challenging due to its limited temporal resolution in many applications. Data-dr…

physics.flu-dyn2026

Real-Time Estimation of High-Resolution Flow Fields and Reduced-Order Coordinates from Event-Based Imaging Velocimetry

L. Franceschelli, E. Amico, C. E. Willert +3

We propose a data-driven framework to estimate high-resolution (HR) velocity fields and reduced-order flow coordinates from real-time Event-Based Imaging Velocimetry (rt-EBIV). Fas…

physics.flu-dyn2026

Information decomposition for disentangled and interpretable manifold learning of fluid flows via variational autoencoders

Zhiyuan Wang, Iacopo Tirelli, Stefano Discetti +1

We introduce an information-theoretic framework that uses variational autoencoders (VAEs) to extract compact, physically interpretable manifolds from high-dimensional flow-field da…