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|Abstract:||We 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.|
|Citation:||Choi, 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/e2016-02662-3|
|Pages:||1165 - 1180|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||European Physical Journal: Special Topics|
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