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20082026
most citedA mathematical framework for critical transitions: bifurcations, fast-slow systems and stochastic dynamics

422 citations · 2.4k across the 156 of their papers we have counts for

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Showing 2022 · math.DSShow all

9 papers · 2 filters

math.DS2022★ 6 cited

Reduction Methods in Climate Dynamics -- A Brief Review

Felix Hummel, Peter Ashwin, Christian Kuehn

We review a range of reduction methods that have been, or may be useful for connecting models of the Earth's climate system of differing complexity. We particularly focus on method…

math.DS2022★ 12 cited

Entry-exit functions in fast-slow systems with intersecting eigenvalues

Panagiotis Kaklamanos, Christian Kuehn, Nikola Popović +1

We study delayed loss of stability in a class of fast-slow systems with two fast variables and one slow one, where the linearisation of the fast vector field along a one-dimensiona…

math.DS2022★ 2 cited

Geometric Blow-Up for Folded Limit Cycle Manifolds in Three Time-Scale Systems

Samuel Jelbart, Christian Kuehn, Sara-Viola Kuntz

Geometric singular perturbation theory provides a powerful mathematical framework for the analysis of 'stationary' multiple time-scale systems which possess a critical manifold, i.…

math.DS2022★ 1 cited

Geometric blow-up of a dynamic Turing instability in the Swift-Hohenberg equation

Felix Hummel, Samuel Jelbart, Christian Kuehn

We present a rigorous analysis of the slow passage through a Turing bifurcation in the Swift-Hohenberg equation using a novel approach based on geometric blow-up. We show that the…

math.DS2022★ 16 cited

Phase Oscillator Networks with Nonlocal Higher-Order Interactions: Twisted States, Stability and Bifurcations

Christian Bick, Tobias Böhle, Christian Kuehn

The Kuramoto model provides a prototypical framework to synchronization phenomena in interacting particle systems. Apart from full phase synchrony where all oscillators behave iden…

math.DS2022★ 2 cited

Computer Validation of Neural Network Dynamics: A First Case Study

Christian Kuehn, Elena Queirolo

A large number of current machine learning methods rely upon deep neural networks. Yet, viewing neural networks as nonlinear dynamical systems, it becomes quickly apparent that mat…

math.DS2022★ 3 cited

Mean field limits of co-evolutionary signed heterogeneous networks

Marios Antonios Gkogkas, Christian Kuehn, Chuang Xu

Many science phenomena are modelled as interacting particle systems (IPS) coupled on static networks. In reality, network connections are far more dynamic. Connections among indivi…

math.DS2022★ 2 cited

The hyperbolic umbilic singularity in fast-slow systems

Hildeberto Jardón-Kojakhmetov, Christian Kuehn, Maximilian Steinert

Fast-slow systems with three slow variables and gradient structure in the fast variables have, generically, hyperbolic umbilic, elliptic umbilic or swallowtail singularities. In th…

math.DS2022★ 2 cited

Discrete Geometric Singular Perturbation Theory

Samuel Jelbart, Christian Kuehn

We propose a mathematical formalism for discrete multi-scale dynamical systems induced by maps which parallels the established geometric singular perturbation theory for continuous…