Search Results for author:
Found 7 papers, 1 papers with code
Nonlinear model reduction for operator learning
Operator learning provides methods to approximate mappings between infinite-dimensional function spaces.
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.
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.
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.
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
An autoencoder network is used for nonlinear dimension reduction and feature extraction as an alternative for singular value decomposition (SVD).
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.
