5 papers
Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning
Michiel Straat, Thorben Markmann, Sebastian Peitz +1
Chaotic convective flows arise in many real-world systems, such as microfluidic devices and chemical reactors. Stabilizing these flows is highly desirable but remains challenging,…
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime
Thorben Markmann, Michiel Straat, Sebastian Peitz +1
Data-driven flow control has significant potential for industry, energy systems, and climate science. In this work, we study the effectiveness of Reinforcement Learning (RL) for re…
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
Michiel Straat, Thorben Markmann, Barbara Hammer
We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-Bénard Convection (RBC), a model for convection processes that occur in nature and industrial settings. We comp…
Adaptive Kinematic Modeling for Improved Hand Posture Estimates Using a Haptic Glove
Kathrin Krieger, David P. Leins, Thorben Markmann +4
Most commercially available haptic gloves compromise the accuracy of hand-posture measurements in favor of a simpler design with fewer sensors. While inaccurate posture data is oft…
Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection
Thorben Markmann, Michiel Straat, Barbara Hammer
Several related works have introduced Koopman-based Machine Learning architectures as a surrogate model for dynamical systems. These architectures aim to learn non-linear measureme…