An autoencoder‐based reduced‐order model for eigenvalue problems with application to neutron diffusion
Using an autoencoder for dimensionality reduction, this article presents a novel projection‐based reduced‐order model for eigenvalue problems. Reduced‐order modeling relies on finding suitable basis functions which define a low‐dimensional space in which a high‐dimensional system is approximated. Pr...
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Published in | International journal for numerical methods in engineering Vol. 122; no. 15; pp. 3780 - 3811 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
15.08.2021
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Using an autoencoder for dimensionality reduction, this article presents a novel projection‐based reduced‐order model for eigenvalue problems. Reduced‐order modeling relies on finding suitable basis functions which define a low‐dimensional space in which a high‐dimensional system is approximated. Proper orthogonal decomposition (POD) and singular value decomposition (SVD) are often used for this purpose and yield an optimal linear subspace. Autoencoders provide a nonlinear alternative to POD/SVD, that may capture, more efficiently, features or patterns in the high‐fidelity model results. Reduced‐order models based on an autoencoder and a novel hybrid SVD‐autoencoder are developed. These methods are compared with the standard POD‐Galerkin approach and are applied to two test cases taken from the field of nuclear reactor physics. |
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Bibliography: | Funding information EPSRC, MUFFINS (EP/P033180/1); MAGIC (EP/N010221/1); INHALE (EP/T003189/1); PREMIERE (EP/T000414/1) |
ISSN: | 0029-5981 1097-0207 |
DOI: | 10.1002/nme.6681 |