439 citations · 2.2k across the 38 of their papers we have counts for
38 papers
A perturbation-based approach to identifying potentially superfluous network constituents
Timo Bröhl, Klaus Lehnertz
Constructing networks from empirical time series data is often faced with the as yet unsolved issue of how to avoid potentially superfluous network constituents. Such constituents…
Data-Driven Reconstruction of Stochastic Dynamical Equations based on Statistical Moments
Farnik Nikakhtar, Laya Parkavousi, Muhammad Sahimi +3
Stochastic processes are encountered in many contexts, ranging from generation sizes of bacterial colonies and service times in a queueing system to displacements of Brownian parti…
Ordinal methods for a characterization of evolving functional brain networks
Klaus Lehnertz
Ordinal time series analysis is based on the idea to map time series to ordinal patterns, i.e., order relations between the values of a time series and not the values themselves, a…
Arbitrary-order finite-time corrections for the Kramers-Moyal operator
Leonardo Rydin Gorjão, Dirk Witthaut, Klaus Lehnertz +1
With the aim of improving the reconstruction of stochastic evolution equations from empirical time-series data, we derive a full representation of the generator of the Kramers-Moya…
Enhancing the accuracy of a data-driven reconstruction of bivariate jump-diffusion models with corrections for higher orders of the sampling interval
Esra Aslim, Thorsten Rings, Lina Zabawa +1
We evaluate the significance of a recently proposed bivariate jump-diffusion model for a data-driven characterization of interactions between complex dynamical systems. For various…
FitzHugh-Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena
M. Gerster, R. Berner, J. Sawicki +5
We study patterns of partial synchronization in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in human subjects. We report the spontaneou…