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Dimension reduction in heterogeneous neural networks: Generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)

Author(s): Choi, M; Bertalan, T; Laing, CR; Kevrekidis, Yannis G.

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dc.contributor.authorChoi, M-
dc.contributor.authorBertalan, T-
dc.contributor.authorLaing, CR-
dc.contributor.authorKevrekidis, Yannis G.-
dc.date.accessioned2021-10-08T19:58:28Z-
dc.date.available2021-10-08T19:58:28Z-
dc.date.issued2016-09-01en_US
dc.identifier.citationChoi, M, Bertalan, T, Laing, CR, Kevrekidis, YG. (2016). Dimension reduction in heterogeneous neural networks: Generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA). European Physical Journal: Special Topics, 225 (6-7), 1165 - 1180. doi:10.1140/epjst/e2016-02662-3en_US
dc.identifier.issn1951-6355-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1xg25-
dc.description.abstractWe propose, and illustrate via a neural network example, two different approaches to coarse-graining large heterogeneous networks. Both approaches are inspired from, and use tools developed in, methods for uncertainty quantification (UQ) in systems with multiple uncertain parameters – in our case, the parameters are heterogeneously distributed on the network nodes. The approach shows promise in accelerating large scale network simulations as well as coarse-grained fixed point, periodic solution computation and stability analysis. We also demonstrate that the approach can successfully deal with structural as well as intrinsic heterogeneities.en_US
dc.format.extent1165 - 1180en_US
dc.language.isoen_USen_US
dc.relation.ispartofEuropean Physical Journal: Special Topicsen_US
dc.rightsAuthor's manuscripten_US
dc.titleDimension reduction in heterogeneous neural networks: Generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)en_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1140/epjst/e2016-02662-3-
dc.identifier.eissn1951-6401-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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