68 citations · 87 across the 9 of their papers we have counts for
7 papers · 1 filter
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,…
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…
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…