The HydroGym environment for fluid mechanics and zero-shot deep reinforcement learning!

Conditional flow matching to yield the highest drag reduction in turbulence!!

Explainable deep reinforcement learning discovers control strategies at a reduced energetic cost!!

The AIAA Community Challenge on ML and fluid mechanics!

Artificial intelligence is revolutionizing science!!

The role of artificial intelligence in achieving the Sustainable Development Goals

An AI based framework for harmonizing climate policies and projects with the SDGs

Seminar Ramón Pozuelo High performance computing and turbulence with pressure gradients

Seminar Esteban Ferrer New avenues in Computational Fluid Dynamics

Seminar Francesca Larosa: Artificial intelligence and climate action

Deep reinforcement learning to control Rayleigh Bénard convection Part 1 4 baseline convergence

Deep reinforcement learning to control Rayleigh Bénard convection Part 3 4 MARL controller

Deep reinforcement learning to control Rayleigh Bénard convection Part 4 4 coalesced cell

Deep reinforcement learning to control Rayleigh Bénard convection Part 2 4 SARL controller

Explaining turbulence through deep learning

Seminar by Patricio Clark Di Leoni Reconstruction and generation of turbulent states

Seminar by F. Chinesta Informing learning for empowering the engineering of critical urban systems

Studying structures in urban flows through machine learning

High fidelity simulations of wing tip vortices

Causality analysis of turbulent structures in urban environments

Keynote lecture ParCFD 2024

Keynote lecture at the European Turbulence Conference (ETC18) in Valencia, Spain

Energy transfer mechanisms in turbulent boundary layers

SHAP-based identification of coherent structures

Optimal sensor placement with diffusion models and explainable deep learning

Optimal sensor placement

QR pivoting for optimal sensor placement

Robust principal component analysis

Explainable deep learning tutorial!

Compressed sensing

Introduction to machine learning, Part 5 The proper orthogonal decomposition POD

Large language models for problems in Physics

Probability in physics

Transformers for physical systems

A new transformer for chaotic systems: easy attention!

Applications of Compressed sensing

Extension of Compressed sensing

Compressed sensing

Theory of the dynamic mode decomposition (DMD)

Introduction to the dynamic mode decomposition (DMD)

Applications of the sparse identification of nonlinear dynamics (SINDy)

Sparse identification of nonlinear dynamical systems (SINDy)

DMD and Control

The DMD Algorithm

Deep reinforcement learning for flow control on a cylinder at Re=2,000

Deep reinforcement learning for flow control in aeronautics

Reduced-order models of the Navier Stokes equations

The Galerkin projection

Reduced-order models for spatio-temporal decompositions

Introduction to reduced-order models

Learning turbulence in channels to control wings!!

High fidelity simulation of wing tip vortices

Introduction to machine learning, Part 5 The proper orthogonal decomposition POD

Introduction to machine learning, Part 4 The Method of Snapshots

Introduction to machine learning, Part 3 Truncated SVD and eigendecomposition

Introduction to machine learning, Part 2 The economy singular value decomposition SVD

Introduction to machine learning, Part 1 The singular value decomposition SVD

Pressure gradient turbulent boundary layers around wing sections

Deep learning for spatial predictions in turbulent flows

The physics of airplane wings and history of the flow