2 citations · 5 across the 7 of their papers we have counts for
9 papers
Adjusting for Social Groups in Spatial Point Process Models for Waiting Pedestrian Configurations
Lars Sickert Karam, Rui M. Castro, Maarten Schoukens +1
The spatial distribution of waiting pedestrians has two primary drivers: environmental preference and interaction with other pedestrians. Spatial point processes naturally capture…
Modeling inhomogeneous spatial point configurations with applications to replicated patterns in real-life waiting crowds
Lars Sickert Karam, Rui M. Castro, Maarten Schoukens +1
In this article, we connect statistical inference for spatial point processes with the analysis of waiting pedestrian crowds through two interconnected contributions. First, on the…
Efficient stochastic model-predictive control based on the meta-state-space representation
Bendegúz Györök, Roland Tóth, Maarten Schoukens +1
Stochastic model-predictive control (SMPC) has evolved to a powerful framework for the control of stochastic dynamical systems. SMPC utilizes a probabilistic uncertainty descriptio…
Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
Bendegúz Györök, Roel Drenth, Chris Verhoek +3
The integration of first-principles models with learning-based components, i.e., model augmentation, has gained increasing attention, as it offers higher model accuracy and faster…
A frequency-domain approach for estimating continuous-time diffusively coupled linear networks
Desen Liang, E. M. M., Kivits +2
This paper addresses the problem of consistently estimating a continuous-time (CT) diffusively coupled network (DCN) to identify physical components in a physical network. We devel…
Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics
Dilan Ozturk, Pepijn Saraber, Kevin Bielawski +4
Biological tissue integrity is actively maintained by cells. It is essential to comprehend how cells accomplish this in order to stage tissue diseases. However, addressing the comp…
Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
Thomas de Jong, Valentina Breschi, Maarten Schoukens +1
In this paper, we consider the design of data-driven predictive controllers for nonlinear systems from input-output data via linear-in-control input Koopman lifted models. Instead…
Variational message passing for online polynomial NARMAX identification
Wouter Kouw, Albert Podusenko, Magnus Koudahl +1
We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated…
Improved Initialization of State-Space Artificial Neural Networks
Maarten Schoukens
The identification of black-box nonlinear state-space models requires a flexible representation of the state and output equation. Artificial neural networks have proven to provide…