activity
20162024
most citedRecent advances in applying deep reinforcement learning for flow control: perspectives and future directions

157 citations · 544 across the 37 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

cs.CY2020

Towards an Ethical Framework in the Complex Digital Era

David Pastor-Escuredo, Ricardo Vinuesa

The digital revolution has brought ethical crossroads of technology, behavior and truth. However, the need of a comprehensive and constructive ethical framework is emerging as digi…

physics.comp-ph2020★ 1 cited

An Uncertainty-Quantification Framework for Assessing Accuracy, Sensitivity, and Robustness in Computational Fluid Dynamics

Saleh Rezaeiravesh, Ricardo Vinuesa, Philipp Schlatter

A framework is developed based on different uncertainty quantification (UQ) techniques in order to assess validation and verification (V&V) metrics in computational physics problem…

physics.flu-dyn2020★ 22 cited

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…

physics.flu-dyn2020

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…

physics.flu-dyn2020

SPOD and resolvent analysis of near-wall coherent structures in turbulent pipe flows

Leandra Abreu, André Cavalieri, Philipp Schlatter +2

Direct numerical simulations, performed with a high-order spectral-element method, are used to study coherent structures in turbulent pipe flow at friction Reynolds numbers $Re_τ =…

physics.flu-dyn2020

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…