2022/08/05 by Azzedine Abdedou, Abdedou, Azzedine, Azzeddine Soulaïmani +1
Environmental Science · Physics and Astronomy · #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Hydrological Forecasting Using AI #Model Reduction and Neural Networks #Wind and Air Flow Studies
paper · pdf · doi:10.48550/arxiv.2208.03190
openalex publication_date 2022/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 stochastic spatio-temporal large-scale physical problems. The method uses two 1d-convolutional autoencoders (CAEs) to reduce the spatial and temporal dimensions from a set of high-fidelity snapshots collected from the high-fidelity numerical solver. The encoded latent vectors, generated from two compression levels, are then mapped to the input parameters using a regression-based multilayer perceptron (MLP). The accuracy of the proposed approach is compared to that of the linear reduced-order technique-based artificial neural network (POD-ANN) through two benchmark tests (the one-dimensional Burgers and Stoker's solutions) and a hypothetical dam-break flow problem, with an unstructured mesh and over a complex bathymetry river. The numerical results show that the proposed nonlinear framework presents strong predictive abilities to accurately approximate the statistical moments of the outputs for complex stochastic large-scale and time-dependent problems, with low computational cost during the predictive online stage.