481 citations · 730 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Physics-informed deep-learning applications to experimental fluid mechanics
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,…
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
Hamidreza Eivazi, Soledad Le Clainche, Sergio Hoyas +1
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 f…
Physics-informed neural networks for solving Reynolds-averaged Navier$\unicode{x2013}$Stokes equations
Hamidreza Eivazi, Mojtaba Tahani, Philipp Schlatter +1
Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations (PDEs). We employ PINNs for s…
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
Hamidreza Eivazi, Hadi Veisi, Mohammad Hossein Naderi +1
Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit multiple complex phenomena both in time and space. Reduced Order Modeling (ROM) of fluid fl…
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…