activity
20102023
most citedDeep Model Predictive Control with Online Learning for Complex Physical Systems

85 citations · 332 across the 23 of their papers we have counts for

collaborators

23 papers

math.OC2023

Multiobjective Optimization of Non-Smooth PDE-Constrained Problems

Marco Bernreuther, Michael Dellnitz, Bennet Gebken +4

Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimizati…

math.DS2023

On the Dynamical Hierarchy in Gathering Protocols with Circulant Topologies

Raphael Gerlach, Sören von der Gracht, Michael Dellnitz

In this article, we investigate the convergence behavior of two classes of gathering protocols with fixed circulant topologies using tools from dynamical systems. Given a fixed num…

math.DS2021★ 8 cited

Efficient time stepping for numerical integration using reinforcement learning

Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke +4

Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamic…

math.OC2019

ROM-based multiobjective optimization of elliptic PDEs via numerical continuation

Stefan Banholzer, Bennet Gebken, Michael Dellnitz +2

Multiobjective optimization plays an increasingly important role in modern applications, where several objectives are often of equal importance. The task in multiobjective optimiza…

cs.LG2019★ 85 cited

Deep Model Predictive Control with Online Learning for Complex Physical Systems

Katharina Bieker, Sebastian Peitz, Steven L. Brunton +2

The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as fl…

math.DS2019★ 1 cited

Revealing the intrinsic geometry of finite dimensional invariant sets of infinite dimensional dynamical systems

Raphael Gerlach, Péter Koltai, Michael Dellnitz

Embedding techniques allow the approximations of finite dimensional attractors and manifolds of infinite dimensional dynamical systems via subdivision and continuation methods. The…