ERC StG - Complete

Y Liu, M Kiss, R Tóth, M Schoukens, On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach, arXiv preprint arXiv:2502.17042.

K Classens, M Schoukens, T Oomen, JP Noël, Locating nonlinearities in mechanical systems: A frequency-domain dynamic network perspective, Mechanical Systems and Signal Processing 224, 112124.

GJE van Otterdijk, S Moradi, S Weiland, R Tóth, NO Jaensson, M Schoukens, Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks, arXiv preprint arXiv:2411.05730.

GI Beintema, M Schoukens, R Tóth, Meta-state–space learning: An identification approach for stochastic dynamical systems, Automatica 167, 111787.

JH Hoekstra, C Verhoek, R Tóth, M Schoukens, Learning-based model augmentation with LFRs, arXiv preprint arXiv:2404.01901.

D Materassi, S Warnick, C Rojas, M Schoukens, E Cross, Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part I), IFAC-PapersOnLine 58 (15), 492-496.

D Materassi, S Warnick, C Rojas, M Schoukens, E Cross, Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part II), IFAC-PapersOnLine 58 (15), 497-501.

M Kiss, R Tóth, M Schoukens, Space-Filling Input Design for Nonlinear State-Space Identification, IFAC-PapersOnLine 58 (15), 562-567.

MD Champneys, GI Beintema, R Tóth, M Schoukens, TJ Rogers, Baseline Results for Selected Nonlinear System Identification Benchmarks, IFAC-PapersOnLine 58 (15), 474-479.

Y Liu, R Tóth, M Schoukens, Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks, IFAC-PapersOnLine 58 (15), 295-300.