A non-intrusive reduced-order modeling 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 (Comput. Math. Appl., 2021)

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 (CoRR, 2021)

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; Azzeddine Soulaïmani (J. Comput. Phys., 2021)

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders. - Azzedine Abdedou; Azzeddine Soulaïmani (Adv. Model. Simul. Eng. Sci., 2023)

Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder. - Shubham Chaudhry; Azzedine Abdedou; Azzeddine Soulaïmani (Adv. Model. Simul. Eng. Sci., 2025)