Hi! I’m Michiel. I’m a researcher at Bielefeld University.
My research focuses on learning theory, physics-informed machine learning, reinforcement learning, surrogate modeling, and machine learning for smart manufacturing.
Before joining Bielefeld University, I worked at the University of Groningen on learning theory for neural networks and predictive maintenance.
Postdoc and Junior Research Group Leader
Machine Learning group, Bielefeld University
PhD in Theoretical Machine Learning and Industry 4.0
Intelligent Systems group, University of Groningen, PhD thesis
MSc. in Computing Science (specialization: Machine Learning)
University of Groningen
We show that the use Hermite polynomials facilitate the analysis of on-line learning scenarios for arbitrary activation functions.
We build a robust reinforcement learning agent to control Rayleigh-Bénard Convection in turbulent conditions and compare against conventional control methods.
We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-B’enard Convection (RBC), a model for convection processes that occur in nature and industrial settings and that yields zero-shot super resolution. We compare the prediction accuracy and model properties of FNO surrogates to two popular surrogates used in fluid dynamics.
In order to compute weight updates, backpropagation uses complete knowledge of the downstream weights in the network. In [1] it is …
A multidisciplinary project focusing on transparent AI, human agency, safety and resource-efficiency.
Typical case analysis of machine learning scenarios
Employing AI techniques to model and control dynamical systems.
Using Machine Learning methods to improve health care
Focuses on the development of intelligent, connected and customized production processes