  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.
