Please use this identifier to cite or link to this item:
http://repositorio.unicamp.br/jspui/handle/REPOSIP/342886
Type: | Artigo |
Title: | Deep neural networks for the construction of reduced order models of compressible flows |
Author: | Lui, Hugo Wolf, William |
Abstract: | In this work, we present a numerical methodology for construction of reduced order models of compressible flows which combines flow modal decomposition via proper orthogonal decomposition and regression analysis using deep feedforward neural networks. The framework is implemented in the context of the sparse identification of non-linear dynamics algorithm recently proposed in the literature. The method is tested on the reconstruction of a canonical nonlinear oscillator and the compressible flow past a cylinder. Results demonstrate that the technique provides accurate and stable reconstructions of the full order model beyond the training window of the deep feedforward neural network, demonstrating the robustness of the current reduced order model |
Subject: | Análise de regressão Dinâmica não linear Redes neurais (Computação) |
Editor: | American institute of aeronautics and astronautics |
Rights: | Fechado |
Identifier DOI: | 10.2514/6.2019-1407 |
Address: | https://arc.aiaa.org/doi/abs/10.2514/6.2019-1407 |
Date Issue: | 2019 |
Appears in Collections: | FEM - Artigos e Outros Documentos |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.