Clustering-Based Model Order Reduction for Nonlinear Network Systems

03/16/2020
by   Peter Benner, et al.
0

Clustering by projection has been proposed as a way to preserve network structure in linear multi-agent systems. Here, we extend this approach to a class of nonlinear network systems. Additionally, we generalize our clustering method which restores the network structure in an arbitrary reduced-order model obtained by projection. We demonstrate this method on a number of examples.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/16/2003

A neural network and iterative optimization hybrid for Dempster-Shafer clustering

In this paper we extend an earlier result within Dempster-Shafer theory ...
research
08/05/2023

Learning physics-based reduced-order models from data using nonlinear manifolds

We present a novel method for learning reduced-order models of dynamical...
research
01/28/2021

Projection based model reduction for the immersed boundary method

Fluid-structure interactions are central to many bio-molecular processes...
research
09/16/2021

Neural-network acceleration of projection-based model-order-reduction for finite plasticity: Application to RVEs

Compared to conventional projection-based model-order-reduction, its neu...
research
05/16/2003

Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure

In this paper we extend an earlier result within Dempster-Shafer theory ...
research
08/31/2023

Detecting Evidence of Organization in groups by Trajectories

Effective detection of organizations is essential for fighting crime and...
research
01/10/2020

Probabilistic K-means Clustering via Nonlinear Programming

K-means is a classical clustering algorithm with wide applications. Howe...

Please sign up or login with your details

Forgot password? Click here to reset