Erik Andreas Martens

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Senior Lecturer (Associate Professor)

Lund University

Sweden

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Erik Andreas Martens is a Senior Lecturer (Associate Professor) at Lund University in Sweden. His research areas include adaptive dynamical networks, complex systems, and their applications in biomedical contexts. Recent publications highlight his work on the adaptive Kuramoto model, vascular network dynamics, and co-evolutionary dynamics of neural networks. He actively contributes to advancing the understanding of bifurcations and nonlinear dynamics within these frameworks.

Recent Grants

Grant: Close

Developing a mathematical theory for co-evolutionary dynamical networks

Open Date: 2023-01-01

Close Date: 2025-01-01

Grant: Close

Synchronization in co-Evolutionary Network Dynamics (SEND)

Open Date: 2019-09-01

Close Date: 2022-01-01

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2020

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2020

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2020

Collaborators (11)

André H. Erhardt

Weierstrass Institute for Applied Analysis and Stochastics

GERMANY

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Andrew James Samson

Københavns Universitet

DENMARK

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Frederik Filip Stæger

Københavns Universitet

DENMARK

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Edna Toro Garza

Stanford University

UNITED STATES

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Christian Bick

NETHERLANDS

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Poul G. Hjorth

Technical University of Denmark

DENMARK

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Christian Henriksen

Technical University of Denmark

DENMARK

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John H. Thomas

Professor Emeritus

University of Rochester

UNITED STATES

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Jeffrey Tithof

Assistant Professor

University of Minnesota

UNITED STATES

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Ting Du

University of Rochester

UNITED STATES

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Christian Kuehn

GERMANY

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Erik Andreas Martens

Research Interests

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About

Recent Grants

Articles (19)

Focus issue on recent advances in adaptive dynamical networks

Continuum limit of the adaptive Kuramoto model

Non-local transitions and ground state switching in the self-organization of vascular networks

Co-evolutionary dynamics for two adaptively coupled Theta neurons

Inverse stochastic resonance in adaptive small-world neural networks

Erratum: “Bifurcations in adaptive vascular networks: Towards model calibration” [Chaos 33, 093135 (2023)]

Integrability of a Globally Coupled Complex Riccati Array

Editorial: Dynamical systems, PDEs and networks for biomedical applications: Mathematical modeling, analysis and simulations

Complex dynamics in adaptive phase oscillator networks

Bifurcations in adaptive vascular networks

Graphop mean-field limits and synchronization for the stochastic Kuramoto model

Symmetry breaking yields chimeras in two small populations of Kuramoto-type oscillators

First-order like phase transition induced by quenched coupling disorder

Birth and destruction of collective oscillations in a network of two populations of coupled type 1 neurons

A two-frequency-two-coupling model of coupled oscillators

Cerebrospinal fluid influx drives acute ischemic tissue swelling

Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks

First-order synchronization transition in a large population of strongly coupled relaxation oscillators

Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review

Collaborators (11)

André H. Erhardt

Andrew James Samson

Frederik Filip Stæger

Edna Toro Garza

Christian Bick

Poul G. Hjorth

Christian Henriksen

John H. Thomas

Jeffrey Tithof

Ting Du

Christian Kuehn

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