72 citations · 74 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2022★ 2 cited
Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders
Azzedine Abdedou, Azzeddine Soulaïmani
A non-intrusive reduced order model based on convolutional autoencoders (NIROM-CAEs) is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stoch…
math.NA2021
A non-intrusive reduced-order modelling for uncertainty propagation of time-dependent problems using a B-splines Bézier elements-based method and Proper Orthogonal Decomposition: application to dam-break flows
Azzedine Abdedou, Azzeddine Soulaïmani
A proper orthogonal decomposition-based B-splines Bézier elements method (POD-BSBEM) is proposed as a non-intrusive reduced-order model for uncertainty propagation analysis for sto…
physics.comp-ph2020★ 72 cited
Non-Intrusive Reduced-Order Modeling Using Uncertainty-Aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to Flood Modeling
Pierre Jacquier, Azzedine Abdedou, Vincent Delmas +1
Deep Learning research is advancing at a fantastic rate, and there is much to gain from transferring this knowledge to older fields like Computational Fluid Dynamics in practical e…