5 papers
Precision Specimen Positioning in Electron Microscopy through Hysteresis Compensation, Iterative Learning, and Vision-Based Sensing
J. S. van Hulst, A. M. C. de Peffer, D. Herceg +4
Electron microscopy requires nanometer-scale specimen positioning over a long stroke. Piezo-stepper actuators are well suited for this task, but their accuracy is limited by hyster…
Constructing VAE Latent Spaces with Prescribed Topology
Jilles S. van Hulst, Jakub M. Tomczak, W. P. M. H. Heemels +1
Variational autoencoders (VAEs) learn low-dimensional latent representations of high-dimensional data. When the data lies on a manifold with non-Euclidean topology, the standard Ga…
Estimating Evolving Functions with Dynamic Gaussian Processes
J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
This paper develops the Dynamic Gaussian Process (DGP), a framework for estimating functions governed by integro-difference equations (IDEs). IDEs model continuous functions that e…
Bridging the Simulation-to-Reality Gap in Electron Microscope Calibration via VAE-EM Estimation
Jilles S. van Hulst, W. P. M. H. Heemels, Duarte J. Antunes
Electron microscopy has enabled many scientific breakthroughs across multiple fields. A key challenge is the tuning of microscope parameters based on images to overcome optical abe…
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models
J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
Reinforcement learning (RL) is a powerful framework for decision-making in uncertain environments, but it often requires large amounts of data to learn an optimal policy. We addres…