The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations

DOI10.1016/j.cma.2022.114687OpenAlexW3194202055WikidataQ114952522 ScholiaQ114952522MaRDI QIDQ2138717

Gianluigi Rozza, Michele Girfoglio, Nicola Demo, Davide Papapicco, Giovanni Stabile

Publication date: 12 May 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.06558

zbMATH Keywords

nonlinear hyperbolic equationsmultiphase simulationdeep neural networks (DNNs)reduced order modelling (ROM)shifted-POD (sPOD)

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99) Finite volume methods for initial value and initial-boundary value problems involving PDEs (65M08)

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Uses Software

• redbKIT

Cites Work

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