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arXiv · 2008.10532

An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion

Toby Phillips·Claire E. Heaney·Paul N. Smith·Christopher C. Pain
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Abstract

Using an autoencoder for dimensionality reduction, this paper presents a novel projection-based reduced-order model for eigenvalue problems. Reduced-order modelling 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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BibTeXRIS

Toby Phillips, Claire E. Heaney, Paul N. Smith, Christopher C. Pain. 2020-08-15. An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion. https://doi.org/10.1002/nme.6681

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