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.