Identifying a Wiener system using a variant of the Wiener G-Functionals. - Koen Tiels; Johan Schoukens (CDC/ECC, 2011)

Iterative Update of the Pole Locations in a Wiener-Schetzen Model. - Koen Tiels; Johan Schoukens (ALCOSP, 2013)

From coupled to decoupled polynomial representations in parallel Wiener-Hammerstein models. - Koen Tiels; Johan Schoukens (CDC, 2013)

System identification in a real world. - Johan Schoukens; Anna Marconato; Rik Pintelon; Yves Rolain; Maarten Schoukens; Koen Tiels; Laurent Vanbeylen; Gerd Vandersteen; Anne Van Mulders (AMC, 2014)

Wiener system identification with generalized orthonormal basis functions. - Koen Tiels; Johan Schoukens (Autom., 2014)

Incorporating Best Linear Approximation within LS-SVM-based Hammerstein System Identification. - Ricardo Castro-Garcia; Koen Tiels; Johan Schoukens; Johan A. K. Suykens (CDC, 2015)

Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach. - Philippe Dreesen; Maarten Schoukens; Koen Tiels; Johan Schoukens (I2MTC, 2015)

Structure discrimination in block-oriented models using linear approximations: A theoretic framework. - Johan Schoukens; Rik Pintelon; Yves Rolain; Maarten Schoukens; Koen Tiels; Laurent Vanbeylen; Anne Van Mulders; Gerd Vandersteen (Autom., 2015)

Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. - Koen Tiels; Maarten Schoukens; Johan Schoukens (Autom., 2015)

Hammerstein system identification using LS-SVM and steady state time response. - Ricardo Castro-Garcia; Oscar Mauricio Agudelo; Koen Tiels; Johan A. K. Suykens (ECC, 2016)

Identification of Nonlinear Block-Oriented Systems starting from Linear Approximations: A Survey. - Maarten Schoukens; Koen Tiels (CoRR, 2016)

Nonlinear system identification: Finding structure in nonlinear black-box models. - Philippe Dreesen; Koen Tiels; Mariya Ishteva; Johan Schoukens (CAMSAP, 2017)

Identification of block-oriented nonlinear systems starting from linear approximations: A survey. - Maarten Schoukens; Koen Tiels (Autom., 2017)

Parameter reduction in nonlinear state-space identification of hysteresis. - Alireza Fakhrizadeh Esfahani; Philippe Dreesen; Koen Tiels; Jean-Philippe Noël; Johan Schoukens (CoRR, 2017)

A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets. - Rishi Relan; Koen Tiels; Jean-Marc Timmermans; Johan Schoukens (IEEE Control. Syst. Lett., 2017)

Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example. - Anna Marconato; Maarten Schoukens; Koen Tiels; Widanalage Dhammika Widanage; Amjad Abu-Rmileh; Johan Schoukens (CoRR, 2018)

Nonlinear state-space modelling of the kinematics of an oscillating circular cylinder in a fluid flow. - Jan Decuyper; Tim De Troyer; Mark Runacres; Koen Tiels; Johan Schoukens (CoRR, 2018)

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record. - Rishi Relan; Koen Tiels; Anna Marconato; Philippe Dreesen; Johan Schoukens (CoRR, 2018)

Hammerstein system identification through best linear approximation inversion and regularisation. - Ricardo Castro-Garcia; Koen Tiels; Oscar Mauricio Agudelo; Johan A. K. Suykens (Int. J. Control, 2018)

Deep Convolutional Networks in System Identification. - Carl R. Andersson; Antônio H. Ribeiro; Koen Tiels; Niklas Wahlström; Thomas B. Schön (CDC, 2019)

Sampled-data adaptive observer for state-affine systems with uncertain output equation. - Tarek Ahmed-Ali; Koen Tiels; Maarten Schoukens; Fouad Giri (Autom., 2019)

The trade-off between long-term memory and smoothness for recurrent networks. - Antônio H. Ribeiro; Koen Tiels; Luis Antonio Aguirre; Thomas B. Schön (CoRR, 2019)

Decoupling Multivariate Polynomials for Nonlinear State-Space Models. - Jan Decuyper; Philippe Dreesen; Johan Schoukens; Mark Runacres; Koen Tiels (IEEE Control. Syst. Lett., 2019)

Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. - Antônio H. Ribeiro; Koen Tiels; Luis Antonio Aguirre; Thomas B. Schön (AISTATS, 2020)

On the smoothness of nonlinear system identification. - Antônio H. Ribeiro; Koen Tiels; Jack Umenberger; Thomas B. Schön; Luis Antonio Aguirre (Autom., 2020)

Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling. - Jan Decuyper; Koen Tiels; Mark C. Runacres; Johan Schoukens (CoRR, 2020)

A nonlinear model of vortex-induced forces on an oscillating cylinder in a fluid flow. - Jan Decuyper; Tim De Troyer; Koen Tiels; Johan Schoukens; Mark C. Runacres (CoRR, 2020)

Low-Dimensional Decompositions for Nonlinear Finite Impulse Response Modeling. - Maciej Filinski; Pawel Wachel; Koen Tiels (ICCS (5), 2021)

Decoupling multivariate functions using a non-parametric Filtered CPD approach. - Jan Decuyper; Koen Tiels; Siep Weiland; Johan Schoukens (CoRR, 2021)

PNLSS Toolbox 1.0. - Jan Decuyper; Koen Tiels; Johan Schoukens (CoRR, 2021)

Physics-informed neural networks for modelling anisotropic and bi-anisotropic electromagnetic constitutive laws through indirect data. - Abhishek Chandra; Mitrofan Curti; Koen Tiels; Elena A. Lomonova; Daniel M. Tartakovsky (SSCI, 2022)

Decoupling multivariate functions using a nonparametric filtered tensor decomposition. - Jan Decuyper; Koen Tiels; Siep Weiland; Mark C. Runacres; Johan Schoukens (CoRR, 2022)

Hysteresis Feedforward Compensation: A Direct Tuning Approach Using Hybrid-MEM-Elements. - Nard Strijbosch; Koen Tiels; Tom Oomen (IEEE Control. Syst. Lett., 2022)

Iterative Robust Experiment Design for MIMO System Identification via the S-Lemma. - Nic Dirkx; Koen Tiels; Tom Oomen (CCTA, 2023)

A Computationally Lightweight Safe Learning Algorithm. - Dominik Baumann; Krzysztof Kowalczyk; Koen Tiels; Pawel Wachel (CDC, 2023)

Discovering Sparse Hysteresis Models: A Data-driven Study for Piezoelectric Materials and Perspectives on Magnetic Hysteresis. - Abhishek Chandra; Bram Daniels; Mitrofan Curti; Koen Tiels; Elena A. Lomonova; Daniel M. Tartakovsky (CoRR, 2023)

Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach. - Rodrigo A. González; Koen Tiels; Tom Oomen (CoRR, 2023)

Neural oscillators for magnetic hysteresis modeling. - Abhishek Chandra; Taniya Kapoor; Bram Daniels; Mitrofan Curti; Koen Tiels; Daniel M. Tartakovsky; Elena A. Lomonova (CoRR, 2023)

A Wavelet-Based Approach to FRF Identification From Incomplete Data. - Nic Dirkx; Koen Tiels; Tom Oomen (IEEE Trans. Instrum. Meas., 2023)

Learning low-dimensional separable decompositions of MIMO non-linear systems. - Pawel Wachel; Koen Tiels; Maciej Filinski (Int. J. Control, 2023)

Memory-Element-Based Hysteresis: Identification and Compensation of a Piezoelectric Actuator. - Nard Strijbosch; Koen Tiels; Tom Oomen (IEEE Trans. Control. Syst. Technol., 2023)

Kernel-based identification using Lebesgue-sampled data. - Rodrigo A. González; Koen Tiels; Tom Oomen (Autom., 2024)

Magnetic Hysteresis Modeling with Neural Operators. - Abhishek Chandra; Bram Daniels; Mitrofan Curti; Koen Tiels; Elena A. Lomonova (CoRR, 2024)

Generalizable models of magnetic hysteresis via physics-aware recurrent neural networks. - Abhishek Chandra; Taniya Kapoor; Bram Daniels; Mitrofan Curti; Koen Tiels; Daniel M. Tartakovsky; Elena A. Lomonova (Comput. Phys. Commun., 2025)

Self-Calibrating Position Measurements: Applied to Imperfect Hall Sensors. - Max van Meer; Marijn van Noije; Koen Tiels; Enzo Evers; Lennart Blanken; Gert Witvoet; Tom Oomen (CoRR, 2025)

Safety and optimality in learning-based control at low computational cost. - Dominik Baumann; Krzysztof Kowalczyk; Cristian R. Rojas; Koen Tiels; Pawel Wachel (CoRR, 2025)

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems. - Abhishek Chandra; Taniya Kapoor; Mitrofan Curti; Koen Tiels; Elena A. Lomonova (CoRR, 2025)

Truncated Gaussian Noise Estimation in State-Space Models. - Rodrigo A. González; Angel L. Cedeño; Koen Tiels; Tom Oomen (CoRR, 2025)