2 citations · 2 across the 1 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…