Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders

Azzedine Abdedou; Azzeddine Soulaïmani

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arXiv · 2208.03190

Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders

Azzedine Abdedou·Azzeddine Soulaïmani

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Abstract

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.

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Azzedine Abdedou, Azzeddine Soulaïmani. 2022-08-05. Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders. https://arxiv.org/abs/2208.03190

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