72 citations · 73 across the 3 of their papers we have counts for
3 papers
Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems
Pratyush Bhatt, Yash Kumar, Azzeddine Soulaimani
Physical systems whose dynamics are governed by partial differential equations (PDEs) find applications in numerous fields, from engineering design to weather forecasting. The proc…
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…
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…