1.9k citations · 6.2k across the 179 of their papers we have counts for
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Learning emergent PDEs in a learned emergent space
Felix P. Kemeth, Tom Bertalan, Thomas Thiem +4
We extract data-driven, intrinsic spatial coordinates from observations of the dynamics of large systems of coupled heterogeneous agents. These coordinates then serve as an emergen…
Emergent spaces for coupled oscillators
Thomas N. Thiem, Mahdi Kooshkbaghi, Tom Bertalan +2
In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse variables based on manifold learning algorithms. We illustrate this methodology with the…
Dimension reduction in heterogeneous neural networks: generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)
Minseok Choi, Tom Bertalan, Carlo R. Laing +1
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…
Modeling Heterogeneity in Networks using Uncertainty Quantification Tools
Karthikeyan Rajendran, Andreas C. Tsoumanis, Constantinos I. Siettos +2
Using the dynamics of information propagation on a network as our illustrative example, we present and discuss a systematic approach to quantifying heterogeneity and its propagatio…
Coarse graining, dynamic renormalization and the kinetic theory of shock clustering
Xingjie Li, Matthew O. Williams, Ioannis G. Kevrekidis +1
We demonstrate the utility of the equation free methodology developed by one of the authors (I.G.K) for the study of scalar conservation laws with disordered initial conditions. Th…
Robust oscillations in SIS epidemics on adaptive networks: Coarse-graining by automated moment closure
Thilo Gross, Ioannis G. Kevrekidis
We investigate the dynamics of an epidemiological susceptible-infected-susceptible (SIS) model on an adaptive network. This model combines epidemic spreading (dynamics on the netwo…