Critical Transition Analysis of the Deterministic Wind-Driven Ocean Circulation - A Flux-Based Network Approach. - Jan Viebahn; Henk A. Dijkstra (Int. J. Bifurc. Chaos, 2014)

Sensitivity and resilience of the climate system: A conditional nonlinear optimization approach. - Henk A. Dijkstra; Jan Viebahn (Commun. Nonlinear Sci. Numer. Simul., 2015)

OMUSE: Oceanographic multipurpose software environment. - F. Inti Pelupessy; Ben van Werkhoven; Arjen van Elteren; Jan Viebahn; Adam Candy; Simon Portegies Zwart; Henk A. Dijkstra (eScience, 2016)

Continuation of probability density functions using a generalized Lyapunov approach. - Sven Baars; Jan Viebahn; Thomas E. Mulder; Christian Kuehn 001; Fred W. Wubs; Henk A. Dijkstra (J. Comput. Phys., 2017)

Learning to run a Power Network Challenge: a Retrospective Analysis. - Antoine Marot; Benjamin Donnot; Gabriel Dulac-Arnold; Adrian Kelly; Aidan O'Sullivan; Jan Viebahn; Mariette Awad; Isabelle Guyon; Patrick Panciatici; Camilo Romero (NeurIPS (Competition and Demos), 2020)

Exploring grid topology reconfiguration using a simple deep reinforcement learning approach. - Medha Subramanian; Jan Viebahn; Simon H. Tindemans; Benjamin Donnot; Antoine Marot (CoRR, 2020)

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents. - Malte Lehna; Jan Viebahn; Christoph Scholz 001; Antoine Marot; Sven Tomforde (CoRR, 2023)

Hierarchical Reinforcement Learning for Power Network Topology Control. - Blazej Manczak; Jan Viebahn; Herke van Hoof (CoRR, 2023)

Imitation Learning for Intra-Day Power Grid Operation through Topology Actions. - Matthijs de Jong; Jan Viebahn; Yuliya Shapovalova (CoRR, 2024)

Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control. - Yassine El Manyari; Anton Robert Fuxjäger; Stefan Zahlner; Joost van Dijk; Alberto Castagna; Davide Barbieri; Jan Viebahn; Marcel Wasserer (e-Energy, 2025)

Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control. - Barbera de Mol; Davide Barbieri; Jan Viebahn; Davide Grossi (e-Energy, 2025)

Generalizable Graph Neural Networks for Robust Power Grid Topology Control. - Matthijs de Jong; Jan Viebahn; Yuliya Shapovalova (CoRR, 2025)

Accelerated DC loadflow solver for topology optimization. - Nico Westerbeck; Joost van Dijk; Jan Viebahn; Christian Merz; Dirk Witthaut (CoRR, 2025)

Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control. - Yassine El Manyari; Anton R. Fuxjäger; Stefan Zahlner; Joost van Dijk; Alberto Castagna; Davide Barbieri; Jan Viebahn; Marcel Wasserer (CoRR, 2025)

Multi-Objective Reinforcement Learning for Power Grid Topology Control. - Thomas Lautenbacher; Ali Rajaei; Davide Barbieri; Jan Viebahn; Jochen L. Cremer (CoRR, 2025)

Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach. - Mohamed Hassouna; Clara Holzhüter; Malte Lehna; Matthijs de Jong; Jan Viebahn; Bernhard Sick; Christoph Scholz 001 (CoRR, 2025)

A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures. - Milad Leyli-Abadi; Ricardo J. Bessa; Jan Viebahn; Daniel Boos; Clark Borst; Alberto Castagna; Ricardo Chavarriaga; Mohamed Hassouna; Bruno Lemetayer; Giulia Leto; Antoine Marot; Maroua Meddeb; et al. (CoRR, 2025)