Proper orthogonal decomposition and physical field reconstruction with artificial neural networks (ANN) for supercritical flow problems

DOI10.1016/j.enganabound.2022.04.001OpenAlexW4224304821WikidataQ114951749 ScholiaQ114951749MaRDI QIDQ2161608

Yanyan Li

Publication date: 4 August 2022

Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.enganabound.2022.04.001

zbMATH Keywords

order reductionproper orthogonal decomposition (POD)artificial neural network (ANN)field constructionsupercritical fluid flows

Mathematics Subject Classification ID

Fluid mechanics (76-XX) Geophysics (86-XX)

Uses Software

• darch

Cites Work

• A POD-Galerkin reduced order model of a turbulent convective buoyant flow of Sodium over a backward-facing step
• Neural network modeling for near wall turbulent flow.
• A Krylov-based proper orthogonal decomposition method for elastodynamics problems with isogeometric analysis
• Physically interpretable machine learning algorithm on multidimensional non-linear fields
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
• Parametric non-intrusive model order reduction for flow-fields using unsupervised machine learning
• Efficient uncertainty quantification of CFD problems by combination of proper orthogonal decomposition and compressed sensing
• Reducing the Dimensionality of Data with Neural Networks

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