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A finite element reduced-order model based on adaptive mesh refinement and artificial neural networks

Author
Baiges, J.; Codina, R.; Castañar, I.; Castillo, E.
Type of activity
Journal article
Journal
International journal for numerical methods in engineering
Date of publication
2020-02
Volume
121
Number
4
First page
588
Last page
601
DOI
10.1002/nme.6235
Project funding
RYC-2015-17367
Topology Optimization of structures subject to fluid structure interaction
Repository
http://hdl.handle.net/2117/177510 Open in new window
https://www.researchgate.net/publication/335802770_A_Finite_Element_Reduced_Order_Model_based_on_Adaptive_Mesh_Refinement_and_Artificial_Neural_Networks Open in new window
URL
https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.6235 Open in new window
Abstract
In this work, a reduced-order model based on adaptive finite element meshes and a correction term obtained by using an artificial neural network (FAN-ROM) is presented. The idea is to run a high-fidelity simulation by using an adaptively refined finite element mesh and compare the results obtained with those of a coarse mesh finite element model. From this comparison, a correction forcing term can be computed for each training configuration. A model for the correction term is built by using an a...
Citation
Baiges, J. [et al.]. A finite element reduced-order model based on adaptive mesh refinement and artificial neural networks. "International journal for numerical methods in engineering", Febrer 2020, vol. 121, núm. 4, p. 588-601.
Keywords
Adaptivity, Artificial neural network, Finite element methods, Reduced-order model
Group of research
ANiComp - Numerical analysis and scientific computation
Universitat Politècnica de Catalunya

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