2016/03/21 by Minseok Choi, Tom Bertalan, Carlo R. Laing +2
Decision Sciences · Mathematics · Physics and Astronomy · #Analysis of variance #Applied mathematics #Artificial intelligence #Artificial neural network #CHAOS (operating system) #Computer science #Dimension (graph theory) #Dimensionality reduction #Geometry #Mathematical analysis #Mathematics #Model Reduction and Neural Networks #Polynomial #Polynomial chaos #Probabilistic and Robust Engineering Design #Pure mathematics #Reduction (mathematics) #Statistics #Variance (accounting) #Variance reduction #nlin.AO #stochastic dynamics and bifurcation
paper · pdf · doi:10.1140/epjst/e2016-02662-3
arxiv created 2016/03/21 · openalex publication_date 2016/09/01 · arxiv updated 2016/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 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 and stability analysis. We also demonstrate that the approach can successfully deal with structural as well as intrinsic heterogeneities.