22 citations · 110 across the 17 of their papers we have counts for
17 papers
Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems
Hans Harder, Abhijeet Vishwasrao, Luca Guastoni +2
This paper is concerned with probabilistic techniques for forecasting dynamical systems described by partial differential equations (such as, for example, the Navier-Stokes equatio…
PICT -- A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics
Aleksandra Franz, Hao Wei, Luca Guastoni +1
Despite decades of advancements, the simulation of fluids remains one of the most challenging areas of in scientific computing. Supported by the necessity of gradient information i…
Improving turbulence control through explainable deep learning
Miguel Beneitez, Andres Cremades, Luca Guastoni +1
Turbulent-flow control aims to develop strategies that effectively manipulate fluid systems, such as the reduction of drag in transportation and enhancing energy efficiency, both c…
Deep reinforcement learning for the management of the wall regeneration cycle in wall-bounded turbulent flows
Giorgio Maria Cavallazzi, Luca Guastoni, Ricardo Vinuesa +1
The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforce…
Drag-reduction strategies in wall-bounded turbulent flows using deep reinforcement learning
L. Guastoni, J. Rabault, H. Azizpour +1
In this work we compare different drag-reduction strategies that compute their actuation based on the fluctuations at a given wall-normal location in turbulent open channel flow. I…
Easy attention: A simple attention mechanism for temporal predictions with transformers
Marcial Sanchis-Agudo, Yuning Wang, Roger Arnau +4
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 whic…
Predicting the wall-shear stress and wall pressure through convolutional neural networks
Arivazhagan G. Balasubramanian, Luca Guastoni, Philipp Schlatter +2
The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are perfor…
Deep reinforcement learning for turbulent drag reduction in channel flows
L. Guastoni, J. Rabault, P. Schlatter +2
We introduce a reinforcement learning (RL) environment to design and benchmark control strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The environ…
Direct numerical simulation of a zero-pressure-gradient thermal turbulent boundary layer up to
Arivazhagan G. Balasubramanian, Luca Guastoni, Philipp Schlatter +1
The objective of the present study is to provide a numerical database of thermal boundary layers and to contribute to the understanding of the dynamics of passive scalars at differ…
Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers
L. Guastoni, A. G. Balasubramanian, F. Foroozan +6
Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations…
Predicting the temporal dynamics of turbulent channels through deep learning
Giuseppe Borrelli, Luca Guastoni, Hamidreza Eivazi +2
The success of recurrent neural networks (RNNs) has been demonstrated in many applications related to turbulence, including flow control, optimization, turbulent features reproduct…
Predicting the near-wall region of turbulence through convolutional neural networks
A. G. Balasubramanian, L. Guastoni, A. Güemes +5
Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. A…
Convolutional-network models to predict wall-bounded turbulence from wall quantities
L. Guastoni, A. Güemes, A. Ianiro +4
Two models based on convolutional neural networks are trained to predict the two-dimensional velocity-fluctuation fields at different wall-normal locations in a turbulent open chan…
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
Hamidreza Eivazi, Luca Guastoni, Philipp Schlatter +2
The capabilities of recurrent neural networks and Koopman-based frameworks are assessed in the prediction of temporal dynamics of the low-order model of near-wall turbulence by Moe…
On the use of recurrent neural networks for predictions of turbulent flows
Luca Guastoni, Prem A. Srinivasan, Hossein Azizpour +2
In this paper, the prediction capabilities of recurrent neural networks are assessed in the low-order model of near-wall turbulence by Moehlis {\it et al.} (New J. Phys. {\bf 6}, 5…
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
L. Guastoni, M. P. Encinar, P. Schlatter +2
A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-sh…
Predictions of turbulent shear flows using deep neural networks
P. A. Srinivasan, L. Guastoni, H. Azizpour +2
In the present work we assess the capabilities of neural networks to predict temporally evolving turbulent flows. In particular, we use the nine-equation shear flow model by Moehli…