Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem-Reference-Cited by-同舟云学术

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Published:2019-05 Issue: Volume:384 Page:289-307
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Wang Qian^ORCID,Hesthaven Jan S.^ORCID,Ray Deep^ORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modelling and Simulation,Numerical Analysis

Reference57 articles.

1. A survey of projection-based model reduction methods for parametric dynamical systems;Benner;SIAM Rev.,2015

2. Reduced Order Methods for Modeling and Computational Reduction, vol. 9;Quarteroni,2014

3. Certified Reduced Basis Methods for Parametrized Partial Differential Equations;Hesthaven,2016

4. Reduced basis method for the rapid and reliable solution of partial differential equations;Maday,2006

5. The proper orthogonal decomposition in the analysis of turbulent flows;Berkooz;Annu. Rev. Fluid Mech.,1993

Cited by 178 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatial–temporal prediction model for unsteady near-wall flow around cylinder based on hybrid neural network;Computers & Fluids;2024-11

2. Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers;International Journal of Non-Linear Mechanics;2024-09

3. Reduced order modelling of intracranial aneurysm flow using proper orthogonal decomposition and neural networks;International Journal for Numerical Methods in Biomedical Engineering;2024-08-18

4. Local Surrogate Modeling for Spatial Emulation of Gas-Turbine Combustion Via Similarity-Based Sample Processing;Journal of Engineering for Gas Turbines and Power;2024-08-16

5. Reduction of the shallow water system by an error aware POD-neural network method: Application to floodplain dynamics;Computer Methods in Applied Mechanics and Engineering;2024-08