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.
Original language | English (US) |
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Pages (from-to) | 1165-1180 |
Number of pages | 16 |
Journal | European Physical Journal: Special Topics |
Volume | 225 |
Issue number | 6-7 |
DOIs | |
State | Published - Sep 1 2016 |
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Physics and Astronomy(all)
- Physical and Theoretical Chemistry