World's Best Scientists 2026 revealed!
Michael Dellnitz

Michael Dellnitz

D-Index & Metrics

Mathematics

D-Index
38
Citations
6080
World Ranking
2343
National Ranking
143

Overview

Michael Dellnitz is affiliated with the University of Paderborn in Germany and has contributed to multiple fields including Engineering, Computer Science, and Physics and Astronomy. Their research spans subfields such as Control and Systems Engineering, Statistical and Nonlinear Physics, Computer Networks and Communications, Numerical Analysis, and Computational Mechanics.

Their work frequently addresses topics like Model Reduction and Neural Networks, Advanced Control Systems Optimization, Iterative Learning Control Systems, Probabilistic and Robust Engineering Design, Numerical methods for differential equations, Distributed Control Multi-Agent Systems, and Lattice Boltzmann Simulation Studies.

Recent publications by Michael Dellnitz include:

  • Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning, 2023, SIAM Journal on Scientific Computing
  • Efficient time stepping for numerical integration using reinforcement learning, 2021, arXiv (Cornell University)
  • Deep model predictive flow control with limited sensor data and online learning, 2020, Theoretical and Computational Fluid Dynamics
  • A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors, 2020, SIAM Journal on Applied Dynamical Systems
  • Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults, 2021, Cognitive Neurodynamics

Frequent co-authors collaborating with Michael Dellnitz include:

  • Sebastian Peitz
  • Raphael Gerlach
  • Eyke Hüllermeier
  • Marvin Lücke
  • Sina Ober-Blöbaum

Michael Dellnitz has published mainly in venues such as arXiv (Cornell University), Theoretical and Computational Fluid Dynamics, SIAM Journal on Scientific Computing, SIAM Journal on Applied Dynamical Systems, and Cognitive Neurodynamics.

Best Publications

  • On the Approximation of Complicated Dynamical Behavior

    Michael Dellnitz;Oliver Junge

  • A subdivision algorithm for the computation of unstable manifolds and global attractors

    Michael Dellnitz;Andreas Hohmann

  • A SURVEY OF METHODS FOR COMPUTING (UN)STABLE MANIFOLDS OF VECTOR FIELDS

    Bernd Krauskopf;Hinke M. Osinga;Eusebius J. Doedel;Michael E. Henderson

  • Chapter 5 - Set Oriented Numerical Methods for Dynamical Systems

    Michael Dellnitz;Oliver Junge

  • The Algorithms Behind GAIO — Set Oriented Numerical Methods for Dynamical Systems

    Michael Dellnitz;Gary Froyland;Oliver Junge

  • Covering Pareto Sets by Multilevel Subdivision Techniques

    M. Dellnitz;O. Schütze;T. Hestermeyer

  • Detecting and Locating Near-Optimal Almost-Invariant Sets and Cycles

    Gary Froyland;Michael Dellnitz

  • Transport in Dynamical Astronomy and Multibody Problems

    Michael Dellnitz;Oliver Junge;Wang Sang Koon;Francois Lekien

  • Covering pareto sets by multilevel evolutionary subdivision techniques

    Oliver Schütze;Sanaz Mostaghim;Michael Dellnitz;Jürgen Teich

  • Exploring invariant sets and invariant measures.

    Michael Dellnitz;Andreas Hohmann;Oliver Junge;Martin Rumpf

  • The Computation of Unstable Manifolds Using Subdivision and Continuation

    Michael Dellnitz;Andreas Hohmann

  • On the isolated spectrum of the Perron-Frobenius operator

    Michael Dellnitz;Gary Froyland;Stefan Sertl

  • Computation of Essential Molecular Dynamics by Subdivision Techniques

    Peter Deuflhard;Michael Dellnitz;Oliver Junge;Christof Schütte

  • On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems

    Oliver Schütze;Alessandro Dell'Aere;Michael Dellnitz

  • Seasonal variability of the subpolar gyres in the Southern Ocean: a numerical investigation based on transfer operators

    M. Dellnitz;G. Froyland;C. Horenkamp;K. Padberg-Gehle

  • An adaptive subdivision technique for the approximation of attractors and invariant measures

    Michael Dellnitz;Oliver Junge

  • The Structure of Symmetric Attractors

    Ian Melbourne;Michael Dellnitz;Martin Golubitsky

  • Convergence of stochastic search algorithms to finite size pareto set approximations

    Oliver Schütze;Marco Laumanns;Carlos A. Coello Coello;Michael Dellnitz

  • Locating all the zeros of an analytic function in one complex variable

    Michael Dellnitz;Oliver Schütze;Qinghua Zheng

  • Designing optimal low-thrust gravity-assist trajectories using space pruning and a multi-objective approach

    Oliver Schütze;Massimiliano Vasile;Oliver Junge;Michael Dellnitz

  • A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

    Sebastian Peitz;Michael Dellnitz

Frequent Co-Authors

Christof Schütte
Christof Schütte Freie Universität Berlin
Jerrold E. Marsden
Jerrold E. Marsden California Institute of Technology
Martin Golubitsky
Martin Golubitsky The Ohio State University
Ian Melbourne
Ian Melbourne University of Warwick
Gary Froyland
Gary Froyland University of New South Wales
Stefan Volkwein
Stefan Volkwein University of Konstanz
Martin Rumpf
Martin Rumpf University of Bonn
J. Nathan Kutz
J. Nathan Kutz University of Washington
Peter Deuflhard
Peter Deuflhard Zuse Institute Berlin
Joachim Bocker
Joachim Bocker University of Paderborn

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Mathematics in the USA, exploring related online degrees can open doors to diverse career opportunities. Programs like masters in finance online programs blend quantitative skills with financial expertise, ideal for those interested in financial analysis, risk management, or investment banking.

Alternatively, if leadership and business strategy appeal to you, consider accelerated online MBA programs. These programs enable working professionals and students to advance their careers rapidly, gaining essential management skills alongside mathematical knowledge.

Another growing field is marketing analytics, where a strong math background is invaluable. Pursuing a masters in digital marketing equips students to work with data-driven marketing strategies and consumer behavior analysis.

For those seeking a concise yet comprehensive graduate business education, one year MBA programs offer an efficient path. These streamlined degrees combine rigorous coursework with networking opportunities, favoring math students interested in business roles.

Best Scientists Citing Michael Dellnitz

Trending Scientists

Recently Published Articles