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Found 25 papers, 2 papers with code
Indirectly Parameterized Concrete Autoencoders
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly.
Easy attention: A simple self-attention mechanism for transformer-based time-series reconstruction and prediction
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.
Discovering Causal Relations and Equations from Data
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
$β$-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Variational autoencoder (VAE) architectures have the potential to develop reduced-order models (ROMs) for chaotic fluid flows.
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you need
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.
The transformative potential of machine learning for experiments in fluid mechanics
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.
Predicting the wall-shear stress and wall pressure through convolutional neural networks
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.
Identifying regions of importance in wall-bounded turbulence through explainable deep learning
One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow.
Emerging trends in machine learning for computational fluid dynamics
The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research.
Improving aircraft performance using machine learning: a review
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.
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning
In conclusion, our RePIT strategy is a promising technique to reduce the cost of CFD simulations in industry.
Physics-informed deep-learning applications to experimental fluid mechanics
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.
Predicting the temporal dynamics of turbulent channels through deep learning
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.
Aim in Climate Change and City Pollution
The sustainability of urban environments is an increasingly relevant problem.
Enhancing Computational Fluid Dynamics with Machine Learning
Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics.
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
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.
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
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.
Interpretable deep-learning models to help achieve the Sustainable Development Goals
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).
Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations
We first show the applicability of PINNs for solving the Navier-Stokes equations for laminar flows by solving the Falkner-Skan boundary layer.
Remote sensing and AI for building climate adaptation applications
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.
From coarse wall measurements to turbulent velocity fields through deep learning
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
Towards an Ethical Framework in the Complex Digital Era
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.
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
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.
On the use of recurrent neural networks for predictions of turbulent flows
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.
The role of artificial intelligence in achieving the Sustainable Development Goals
We find that AI can support the achievement of 128 targets across all SDGs, but it may also inhibit 58 targets.
