Stefano Discetti

Associate Professor

Universidad Carlos III de Madrid

Spain

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Stefano Discetti is an Associate Professor at Universidad Carlos III de Madrid, Spain. His research areas encompass fluid dynamics, turbulence, and flow estimation techniques, as evidenced by his recent publications on convective heat transfer, meshless methods, and machine learning applications in flow analysis. His work has contributed to advancements in measurement science and technology, evident through his involvement in significant symposiums and award announcements.

Stefano Discetti

Research Interests

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Articles (25)

Announcing the 2024 Measurement Science and Technology outstanding paper awards

Resolving convective velocities of turbulent boundary layer-induced convective heat transfer fluctuations at the wall

Model-based time super-sampling of turbulent flow field sequences

An efficient offline sensor placement method for flow estimation

Full-domain POD modes from PIV asynchronous patches

A meshless method to compute the proper orthogonal decomposition and its variants from scattered data

Advection-based multiframe iterative correction for pressure estimation from velocity fields

Measuring time-resolved heat transfer fluctuations on a heated-thin foil in a turbulent channel airflow

Special issue on the 20th International Symposium on Flow Visualization (ISFV20)

Announcing the 2023 measurement science and technology outstanding paper awards

Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements

Denoising image-based experimental data without clean targets based on deep autoencoders

Self-tuning model predictive control for wake flows

Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks

Machine-learned flow estimation with sparse data—Exemplified for the rooftop of an unmanned aerial vehicle vertiport

Synchronized measurement of instantaneous convective heat flux and velocity fields in wall-bounded flows

Special Issue on Machine learning and data assimilation techniques for fluid flow measurements

A simple trick to improve the accuracy of PIV/PTV data

Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer

Machine learning for flow field measurements: a perspective

From snapshots to manifolds – a tale of shear flows

An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification

Heat transfer enhancement in turbulent boundary layers with a pulsed slot jet in crossflow

Super-resolution generative adversarial networks of randomly-seeded fields

Reducing turbulent convective heat transfer with streamwise plasma vortex generators

Collaborators (13)

Marios Kotsonis

Hongki Yoo

Rodrigo Castellanos

Ricardo Vinuesa

Andrea Ianiro

Andrea Meilán-Vila

OSCAR FLORES

Miguel Alfonso Mendez

fulvio scarano

Bernhard Jakoby

CARLOS SANMIGUEL VILA

Woutijn Baars

Vanesa Guerrero

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