Ricardo Vinuesa | DeepAI

DeepAI AI Chat

Featured Co-authors

Junaid Qadir
65 publications
Gustau Camps-Valls
61 publications
Steven L. Brunton
53 publications
Georg Martius
49 publications
Max Tegmark
36 publications
Hossein Azizpour
25 publications
Virginia Dignum
21 publications
Jakob Runge
21 publications
Romit Maulik
19 publications
Gherardo Varando
17 publications
Karthik Duraisamy
16 publications

∙ 08/24/2023

Easy attention: A simple self-attention mechanism for Transformers

To improve the robustness of transformer neural networks used for tempor...

0 Marcial Sanchis-Agudo, et al. ∙

∙ 05/21/2023

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific...

0 Gustau Camps-Valls, et al. ∙

∙ 04/07/2023

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

Variational autoencoder (VAE) architectures have the potential to develo...

0 Alberto Solera-Rico, et al. ∙

∙ 04/05/2023

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

Rayleigh-Bénard convection (RBC) is a recurrent phenomenon in several in...

0 Colin Vignon, et al. ∙

∙ 03/28/2023

The transformative potential of machine learning for experiments in fluid mechanics

The field of machine learning has rapidly advanced the state of the art ...

2 Ricardo Vinuesa, et al. ∙

∙ 03/13/2023

Closing the gap between research and projects in climate change innovation in Europe

Innovation is a key component to equip our society with tools to adapt t...

0 Francesca Larosa, et al. ∙

∙ 03/01/2023

Predicting the wall-shear stress and wall pressure through convolutional neural networks

The objective of this study is to assess the capability of convolution-b...

0 Arivazhagan G. Balasubramanian, et al. ∙

∙ 02/02/2023

Explaining wall-bounded turbulence through deep learning

Despite its great scientific and technological importance, wall-bounded ...

0 Andres Cremades, et al. ∙

∙ 11/28/2022

Emerging trends in machine learning for computational fluid dynamics

The renewed interest from the scientific community in machine learning (...

0 Ricardo Vinuesa, et al. ∙

∙ 11/04/2022

The Sustainable Development Goals and Aerospace Engineering: A critical note through Artificial Intelligence

The 2030 Agenda of the United Nations (UN) revolves around the Sustainab...

0 Alejandro Sánchez-Roncero, et al. ∙

∙ 10/20/2022

Improving aircraft performance using machine learning: a review

This review covers the new developments in machine learning (ML) that ar...

0 Soledad Le Clainche, et al. ∙

∙ 06/14/2022

Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations

Since the derivation of the Navier Stokes equations, it has become possi...

0 Joongoo Jeon, et al. ∙

∙ 03/29/2022

Physics-informed deep-learning applications to experimental fluid mechanics

High-resolution reconstruction of flow-field data from low-resolution an...

0 Hamidreza Eivazi, et al. ∙

∙ 03/02/2022

Predicting the temporal dynamics of turbulent channels through deep learning

The success of recurrent neural networks (RNNs) has been demonstrated in...

0 Giuseppe Borrelli, et al. ∙

∙ 12/30/2021

Aim in Climate Change and City Pollution

The sustainability of urban environments is an increasingly relevant pro...

17 Pablo Torres, et al. ∙

∙ 10/05/2021

The Potential of Machine Learning to Enhance Computational Fluid Dynamics

Machine learning is rapidly becoming a core technology for scientific co...

184 Ricardo Vinuesa, et al. ∙

∙ 09/16/2021

Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

We use Gaussian stochastic weight averaging (SWAG) to assess the model-f...

9 Masaki Morimoto, et al. ∙

∙ 09/03/2021

Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows

We propose a deep probabilistic-neural-network architecture for learning...

0 Hamidreza Eivazi, et al. ∙

∙ 08/24/2021

Interpretable deep-learning models to help achieve the Sustainable Development Goals

We discuss our insights into interpretable artificial-intelligence (AI) ...

12 Ricardo Vinuesa, et al. ∙

∙ 07/22/2021

Physics-informed neural networks for solving Reynolds-averaged Navierx2013Stokes equations

Physics-informed neural networks (PINNs) are successful machine-learning...

0 Hamidreza Eivazi, et al. ∙

∙ 02/25/2021

COVID-19 Digital Contact Tracing Applications and Techniques: A Review Post Initial Deployments

The coronavirus disease 2019 (COVID-19) is a severe global pandemic that...

0 Muhammad Shahroz, et al. ∙

∙ 10/19/2020

Towards and Ethical Framework in the Complex Digital Era

Since modernity, ethic has been progressively fragmented into specific c...

0 David Pastor-Escuredo, et al. ∙

∙ 05/17/2020

A socio-technical framework for digital contact tracing

In their efforts to tackle the COVID-19 crisis, decision makers are cons...

0 Ricardo Vinuesa, et al. ∙

∙ 02/04/2020

On the use of recurrent neural networks for predictions of turbulent flows

In this paper, the prediction capabilities of recurrent neural networks ...

0 Luca Guastoni, et al. ∙

∙ 04/30/2019

The role of artificial intelligence in achieving the Sustainable Development Goals

The emergence of artificial intelligence (AI) and its progressively wide...

20 Ricardo Vinuesa, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world