68 citations · 87 across the 9 of their papers we have counts for
10 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…
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…
An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data
Michiel Straat, Kevin Koster, Nick Goet +1
Insufficient steel quality in mass production can cause extremely costly damage to tooling, production downtimes and low quality products. Automatic, fast and cheap strategies to e…
Complex-valued embeddings of generic proximity data
Maximilian Münch, Michiel Straat, Michael Biehl +1
Proximities are at the heart of almost all machine learning methods. If the input data are given as numerical vectors of equal lengths, euclidean distance, or a Hilbertian inner pr…
Supervised Learning in the Presence of Concept Drift: A modelling framework
Michiel Straat, Fthi Abadi, Zhuoyun Kan +3
We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototyp…
Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation
Elisa Oostwal, Michiel Straat, Michael Biehl
We study layered neural networks of rectified linear units (ReLU) in a modelling framework for stochastic training processes. The comparison with sigmoidal activation functions is…
Segmentation of blood vessels in retinal fundus images
Michiel Straat, Jorrit Oosterhof
In recent years, several automatic segmentation methods have been proposed for blood vessels in retinal fundus images, ranging from using cheap and fast trainable filters to compli…
On-line learning dynamics of ReLU neural networks using statistical physics techniques
Michiel Straat, Michael Biehl
We introduce exact macroscopic on-line learning dynamics of two-layer neural networks with ReLU units in the form of a system of differential equations, using techniques borrowed f…