no code implementations • 7 May 2024 • Ricardo Vinuesa, Jean Rabault, Hossein Azizpour, Stefan Bauer, Bingni W. Brunton, Arne Elofsson, Elias Jarlebring, Hedvig Kjellstrom, Stefano Markidis, David Marlevi, Paola Cinnella, Steven L. Brunton
Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields.
no code implementations • 22 Apr 2024 • Arivazhagan G. Balasubramanian, Ricardo Vinuesa, Outi Tammisola
Neural-network models have been employed to predict the instantaneous flow close to the wall in a viscoelastic turbulent channel flow.
1 code implementation • 1 Mar 2024 • Alfred Nilsson, Klas Wijk, Sai Bharath Chandra Gutha, Erik Englesson, Alexandra Hotti, Carlo Saccardi, Oskar Kviman, Jens Lagergren, Ricardo Vinuesa, Hossein Azizpour
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly.
no code implementations • 24 Aug 2023 • Marcial Sanchis-Agudo, Yuning Wang, Roger Arnau, Luca Guastoni, Jasmin Lim, Karthik Duraisamy, Ricardo Vinuesa
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention which we demonstrate in time-series reconstruction and prediction.
no code implementations • 21 May 2023 • Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
no code implementations • 7 Apr 2023 • Alberto Solera-Rico, Carlos Sanmiguel Vila, M. A. Gómez, Yuning Wang, Abdulrahman Almashjary, Scott T. M. Dawson, Ricardo Vinuesa
Variational autoencoder (VAE) architectures have the potential to develop reduced-order models (ROMs) for chaotic fluid flows.
1 code implementation • 5 Apr 2023 • Colin Vignon, Jean Rabault, Joel Vasanth, Francisco Alcántara-Ávila, Mikael Mortensen, Ricardo Vinuesa
We show in a case study that MARL DRL is able to discover an advanced control strategy that destabilizes the spontaneous RBC double-cell pattern, changes the topology of RBC by coalescing adjacent convection cells, and actively controls the resulting coalesced cell to bring it to a new stable configuration.
no code implementations • 28 Mar 2023 • Ricardo Vinuesa, Steven L. Brunton, Beverley J. McKeon
The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines.
no code implementations • 1 Mar 2023 • Arivazhagan G. Balasubramanian, Luca Gastonia, Philipp Schlatter, Hossein Azizpour, Ricardo Vinuesa
At $Re_{\tau}=550$, both FCN and R-Net can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at $y^{+} = 50$ using the velocity-fluctuation fields at $y^{+} = 100$ as input with about 10% error in prediction of streamwise-fluctuations intensity.
no code implementations • 2 Feb 2023 • Andres Cremades, Sergio Hoyas, Rahul Deshpande, Pedro Quintero, Martin Lellep, Will Junghoon Lee, Jason Monty, Nicholas Hutchins, Moritz Linkmann, Ivan Marusic, Ricardo Vinuesa
One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow.
no code implementations • 28 Nov 2022 • Ricardo Vinuesa, Steve Brunton
The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research.
no code implementations • 20 Oct 2022 • Soledad Le Clainche, Esteban Ferrer, Sam Gibson, Elisabeth Cross, Alessandro Parente, Ricardo Vinuesa
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring.
no code implementations • 14 Jun 2022 • Joongoo Jeon, Juhyeong Lee, Ricardo Vinuesa, Sung Joong Kim
In conclusion, our RePIT strategy is a promising technique to reduce the cost of CFD simulations in industry.
no code implementations • 29 Mar 2022 • Hamidreza Eivazi, Yuning Wang, Ricardo Vinuesa
High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy.
no code implementations • 2 Mar 2022 • Giuseppe Borrelli, Luca Guastoni, Hamidreza Eivazi, Philipp Schlatter, Ricardo Vinuesa
Alternative reduced-order models (ROMs), based on the identification of different turbulent structures, are explored and they continue to show a good potential in predicting the temporal dynamics of the minimal channel.
no code implementations • 30 Dec 2021 • Pablo Torres, Beril Sirmacek, Sergio Hoyas, Ricardo Vinuesa
The sustainability of urban environments is an increasingly relevant problem.
no code implementations • 5 Oct 2021 • Ricardo Vinuesa, Steven L. Brunton
Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics.
no code implementations • 16 Sep 2021 • Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata
The average of such an ensemble can be regarded as the `mean estimation', whereas its standard deviation can be used to construct `confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks.
no code implementations • 3 Sep 2021 • Hamidreza Eivazi, Soledad Le Clainche, Sergio Hoyas, Ricardo Vinuesa
We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow-field data useful for flow analysis, reduced-order modeling, and flow control.
no code implementations • 24 Aug 2021 • Ricardo Vinuesa, Beril Sirmacek
We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven solutions compliant with the Sustainable Development Goals (SDGs).
no code implementations • 22 Jul 2021 • Hamidreza Eivazi, Mojtaba Tahani, Philipp Schlatter, Ricardo Vinuesa
We first show the applicability of PINNs for solving the Navier-Stokes equations for laminar flows by solving the Falkner-Skan boundary layer.
no code implementations • 6 Jul 2021 • Beril Sirmacek, Ricardo Vinuesa
Urban areas are not only one of the biggest contributors to climate change, but also they are one of the most vulnerable areas with high populations who would together experience the negative impacts.
no code implementations • 12 Mar 2021 • Alejandro Güemes, Hampus Tober, Stefano Discetti, Andrea Ianiro, Beril Sirmacek, Hossein Azizpour, Ricardo Vinuesa
The method is applied both for the resolution enhancement of wall fields and the estimation of wall-parallel velocity fields from coarse wall measurements of shear stress and pressure.
Fluid Dynamics
no code implementations • 19 Oct 2020 • David Pastor-Escuredo, Ricardo Vinuesa
The unequal structure of the global system leads to dynamic changes and systemic problems, which have a more significant impact on those that are most vulnerable.
no code implementations • 1 May 2020 • Hamidreza Eivazi, Luca Guastoni, Philipp Schlatter, Hossein Azizpour, Ricardo Vinuesa
We also observe that using a loss function based only on the instantaneous predictions of the chaotic system can lead to suboptimal reproductions in terms of long-term statistics.
no code implementations • 4 Feb 2020 • Luca Guastoni, Prem A. Srinivasan, Hossein Azizpour, Philipp Schlatter, Ricardo Vinuesa
We also observe that using a loss function based only on the instantaneous predictions of the flow may not lead to the best predictions in terms of turbulence statistics, and it is necessary to define a stopping criterion based on the computed statistics.
no code implementations • 30 Apr 2019 • Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Langhans, Max Tegmark, Francesco Fuso Nerini
We find that AI can support the achievement of 128 targets across all SDGs, but it may also inhibit 58 targets.