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08/24/2023
Easy attention: A simple self-attention mechanism for Transformers
To improve the robustness of transformer neural networks used for tempor...
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05/21/2023
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific...
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04/07/2023
β-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Variational autoencoder (VAE) architectures have the potential to develo...
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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...
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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 ...
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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...
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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...
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02/02/2023
Explaining wall-bounded turbulence through deep learning
Despite its great scientific and technological importance, wall-bounded ...
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11/28/2022
Emerging trends in machine learning for computational fluid dynamics
The renewed interest from the scientific community in machine learning (...
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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...
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10/20/2022
Improving aircraft performance using machine learning: a review
This review covers the new developments in machine learning (ML) that ar...
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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...
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03/29/2022
Physics-informed deep-learning applications to experimental fluid mechanics
High-resolution reconstruction of flow-field data from low-resolution an...
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03/02/2022
Predicting the temporal dynamics of turbulent channels through deep learning
The success of recurrent neural networks (RNNs) has been demonstrated in...
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12/30/2021
Aim in Climate Change and City Pollution
The sustainability of urban environments is an increasingly relevant pro...
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10/05/2021
The Potential of Machine Learning to Enhance Computational Fluid Dynamics
Machine learning is rapidly becoming a core technology for scientific co...
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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...
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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...
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08/24/2021
Interpretable deep-learning models to help achieve the Sustainable Development Goals
We discuss our insights into interpretable artificial-intelligence (AI) ...
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07/22/2021
Physics-informed neural networks for solving Reynolds-averaged Navierx2013Stokes equations
Physics-informed neural networks (PINNs) are successful machine-learning...
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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...
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10/19/2020
Towards and Ethical Framework in the Complex Digital Era
Since modernity, ethic has been progressively fragmented into specific c...
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05/17/2020
A socio-technical framework for digital contact tracing
In their efforts to tackle the COVID-19 crisis, decision makers are cons...
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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 ...
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04/30/2019