Ricardo Vinuesa

KTH Royal Institute of Technology

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Articles (30)

Collaborators (14)

Adrián Lozano-Durán

Massachusetts Institute of Technology

UNITED STATES

Jean Rabault

Norwegian Meteorological Institute

Fermin Mallor

KTH Royal Institute of Technology

Stefano Discetti

Associate Professor

Universidad Carlos III de Madrid

Ramis Örlü

KTH Royal Institute of Technology

Scott Dawson

Illinois Institute of Technology

UNITED STATES

OSCAR FLORES

Universidad Carlos III de Madrid

Hassan Nagib

Rettaliata Professor of Aerospace Engineering

Illinois Institute of Technology

UNITED STATES

Francesco Fuso Nerini

Associate Professor

Royal Institute of Technology

Yuning Wang

KTH Royal Institute of Technology

CARLOS SANMIGUEL VILA

Profesor Asociado

Universidad Carlos III de Madrid

Arnau Miró

Tenure Track Professor

Universitat Politècnica de Catalunya

javier garcia-martinez

Chemistry Professor

Universidad de Alicante

Mikael Mortensen

University of Oslo

Ricardo Vinuesa

Research Interests

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Contact this professor

Articles (30)

Easy attention: A simple attention mechanism for temporal predictions with transformers

Opposition control applied to turbulent wing sections

Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis

Pressure as boundary curvature: A variational approach to potential flows

Inductive biased-deep reinforcement learning methods for flow control: Group-invariant and positional-encoding networks improve learning reproducibility and quality

Large language models in climate and sustainability policy: limits and opportunities

Turbulence around two obstacles in tandem: Effects of obstacle height and separation

Deep-learning-based assessment of skin friction in wall-bounded turbulence

Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning

A new perspective on the simulation of stochastic problems in fluid mechanics with diffusion models

β-Variational autoencoders and transformers for reduced-order modelling of fluid flows

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

Sensitivity study of resolution and convergence requirements for the extended overlap region in wall-bounded turbulence

Physics-informed deep-learning applications to experimental fluid mechanics

Utilizing indicator functions with computational data to confirm nature of overlap in normal turbulent stresses: Logarithmic or quarter-power

Evidence of quasiequilibrium in pressure-gradient turbulent boundary layers

Perspectives on predicting and controlling turbulent flows through deep learning

Direct numerical simulations of a novel device to fight airborne virus transmission

Widest scales in turbulent channels

Towards optimal β-variational autoencoders combined with transformers for reduced-order modelling of turbulent flows

A mission-driven approach for converting research into climate action

Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder

Effective control of two-dimensional Rayleigh–Bénard convection: Invariant multi-agent reinforcement learning is all you need

Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions

Separating large-scale superposition and modulation in turbulent channels

Halting generative AI advancements may slow down progress in climate research

Sentiment analysis on Twitter data towards climate action

The transformative potential of machine learning for experiments in fluid mechanics

General attached eddies: Scaling laws and cascade self-similarity

Enhancement of PIV measurements via physics-informed neural networks

Collaborators (14)

Adrián Lozano-Durán

Jean Rabault

Fermin Mallor

Stefano Discetti

Ramis Örlü

Scott Dawson

OSCAR FLORES

Hassan Nagib

Francesco Fuso Nerini

Yuning Wang

CARLOS SANMIGUEL VILA

Arnau Miró

javier garcia-martinez

Mikael Mortensen

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