Ricardo Vinuesa at KTH Royal Institute of Technology - Research Interests, Publications & Academic Profile

Ricardo Vinuesa

KTH Royal Institute of Technology

Sweden

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Research Interests

Fluid Mechanics

100%

Aerodynamics

100%

Computational Fluid Dynamics

90%

Viscous Flow

80%

Particle Image Velocimetry

70%

Turbulence

50%

Incompressible Flow

50%

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Articles20

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

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2024

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

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2024

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

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2024

Physics-informed deep-learning applications to experimental fluid mechanics

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2024

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

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2024

Evidence of quasiequilibrium in pressure-gradient turbulent boundary layers

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2024

Perspectives on predicting and controlling turbulent flows through deep learning

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2024

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

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2024

Widest scales in turbulent channels

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2024

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

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2024

A mission-driven approach for converting research into climate action

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2023

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

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2023

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

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2023

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

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2023

Separating large-scale superposition and modulation in turbulent channels

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2023

Halting generative AI advancements may slow down progress in climate research

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2023

Sentiment analysis on Twitter data towards climate action

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2023

The transformative potential of machine learning for experiments in fluid mechanics

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2023

General attached eddies: Scaling laws and cascade self-similarity

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2023

Enhancement of PIV measurements via physics-informed neural networks

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Year:

2023

Collaborators10

Adrián Lozano-Durán

Massachusetts Institute of Technology

UNITED STATES

Jean Rabault

Norwegian Meteorological Institute

NORWAY

Stefano Discetti

Associate Professor

Universidad Carlos III de Madrid

SPAIN

Ramis Örlü

KTH Royal Institute of Technology

SWEDEN

Scott Dawson

Illinois Institute of Technology

UNITED STATES

OSCAR FLORES

Universidad Carlos III de Madrid

SPAIN

Francesco Fuso Nerini

Associate Professor

Royal Institute of Technology

SWEDEN

CARLOS SANMIGUEL VILA

Profesor Asociado

Universidad Carlos III de Madrid

SPAIN

javier garcia-martinez

Chemistry Professor

Universidad de Alicante

SPAIN

Mikael Mortensen

Professor

University of Oslo

NORWAY

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