1 code implementation • 27 Mar 2024 • Hamidreza Eivazi, Stefan Wittek, Andreas Rausch
Operator learning provides methods to approximate mappings between infinite-dimensional function spaces.
no code implementations • 29 Mar 2022 • 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, where the measurement data are in general sparse, incomplete and noisy.
no code implementations • 2 Mar 2022 • Giuseppe Borrelli, Luca Guastoni, Hamidreza Eivazi, Philipp Schlatter, Ricardo Vinuesa
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.
no code implementations • 3 Sep 2021 • Hamidreza Eivazi, Soledad Le Clainche, Sergio Hoyas, Ricardo Vinuesa
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.
no code implementations • 22 Jul 2021 • Hamidreza Eivazi, Mojtaba Tahani, Philipp Schlatter, Ricardo Vinuesa
We first show the applicability of PINNs for solving the Navier-Stokes equations for laminar flows by solving the Falkner-Skan boundary layer.
no code implementations • 2 Jul 2020 • Hamidreza Eivazi, Hadi Veisi, Mohammad Hossein Naderi, Vahid Esfahanian
An autoencoder network is used for nonlinear dimension reduction and feature extraction as an alternative for singular value decomposition (SVD).
no code implementations • 1 May 2020 • Hamidreza Eivazi, Luca Guastoni, Philipp Schlatter, Hossein Azizpour, Ricardo Vinuesa
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.