Maarten Schoukens

1.3k total citations
82 papers, 793 citations indexed

About

Maarten Schoukens is a scholar working on Control and Systems Engineering, Civil and Structural Engineering and Artificial Intelligence. According to data from OpenAlex, Maarten Schoukens has authored 82 papers receiving a total of 793 indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Control and Systems Engineering, 22 papers in Civil and Structural Engineering and 20 papers in Artificial Intelligence. Recurrent topics in Maarten Schoukens's work include Control Systems and Identification (56 papers), Fault Detection and Control Systems (29 papers) and Structural Health Monitoring Techniques (22 papers). Maarten Schoukens is often cited by papers focused on Control Systems and Identification (56 papers), Fault Detection and Control Systems (29 papers) and Structural Health Monitoring Techniques (22 papers). Maarten Schoukens collaborates with scholars based in Netherlands, Belgium and Hungary. Maarten Schoukens's co-authors include Koen Tiels, Yves Rolain, Rik Pintelon, J. Schoukens, Jean‐Philippe Noël, Roland Tóth, Gerd Vandersteen, K.R. Godfrey, Torbjörn Wigren and Er‐Wei Bai and has published in prestigious journals such as Automatica, Mechanical Systems and Signal Processing and IEEE Transactions on Instrumentation and Measurement.

In The Last Decade

Maarten Schoukens

78 papers receiving 778 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Maarten Schoukens Netherlands 15 592 201 146 140 108 82 793
Biqiang Mu China 15 473 0.8× 187 0.9× 129 0.9× 88 0.6× 27 0.3× 57 735
Martin Enqvist Sweden 13 557 0.9× 212 1.1× 127 0.9× 65 0.5× 26 0.2× 60 729
Guillaume Mercère France 15 639 1.1× 238 1.2× 70 0.5× 69 0.5× 68 0.6× 74 790
M. Hou United Kingdom 14 931 1.6× 90 0.4× 144 1.0× 71 0.5× 56 0.5× 37 1.1k
Urban Forssell Sweden 10 725 1.2× 144 0.7× 82 0.6× 67 0.5× 30 0.3× 16 914
Gilberto Pin Italy 16 728 1.2× 99 0.5× 55 0.4× 183 1.3× 27 0.3× 79 872
Mohamed Aoun Tunisia 19 1.1k 1.9× 28 0.1× 82 0.6× 90 0.6× 82 0.8× 120 1.4k
H.A. Barker United Kingdom 15 441 0.7× 185 0.9× 84 0.6× 63 0.5× 21 0.2× 55 619
Jean‐Philippe Noël Belgium 16 449 0.8× 799 4.0× 106 0.7× 55 0.4× 120 1.1× 56 1.1k
Stanislav Aranovskiy Russia 17 646 1.1× 70 0.3× 41 0.3× 79 0.6× 28 0.3× 84 790

Countries citing papers authored by Maarten Schoukens

Since Specialization
Citations

This map shows the geographic impact of Maarten Schoukens's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maarten Schoukens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maarten Schoukens more than expected).

Fields of papers citing papers by Maarten Schoukens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maarten Schoukens. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Maarten Schoukens. The network helps show where Maarten Schoukens may publish in the future.

Co-authorship network of co-authors of Maarten Schoukens

This figure shows the co-authorship network connecting the top 25 collaborators of Maarten Schoukens. A scholar is included among the top collaborators of Maarten Schoukens based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Maarten Schoukens. Maarten Schoukens is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tóth, Roland, et al.. (2025). Learning-based model augmentation with LFRs. European Journal of Control. 86. 101304–101304. 1 indexed citations
2.
Schoukens, Maarten, et al.. (2025). Machine learning enhanced tomographic reconstruction for multispectral imaging on TCV. Plasma Physics and Controlled Fusion. 67(2). 25024–25024. 2 indexed citations
3.
Görges, Daniel, et al.. (2024). State Derivative Normalization for Continuous-Time Deep Neural Networks. IFAC-PapersOnLine. 58(15). 253–258. 2 indexed citations
4.
Tóth, Roland, et al.. (2024). Space-Filling Input Design for Nonlinear State-Space Identification. IFAC-PapersOnLine. 58(15). 562–567. 1 indexed citations
5.
Schoukens, Maarten, et al.. (2024). Deep learning of vehicle dynamics. IFAC-PapersOnLine. 58(15). 283–288. 2 indexed citations
6.
Schoukens, Maarten, et al.. (2024). Meta-state–space learning: An identification approach for stochastic dynamical systems. Automatica. 167. 111787–111787. 1 indexed citations
7.
Materassi, Donatello, Sean Warnick, Cristian Rojas, Maarten Schoukens, & Elizabeth J. Cross. (2024). Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part I). IFAC-PapersOnLine. 58(15). 492–496.
8.
Bielawski, Krzysztof, et al.. (2024). Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics. arXiv (Cornell University). 1–6.
9.
Tóth, Roland, et al.. (2024). Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks. IFAC-PapersOnLine. 58(15). 295–300. 4 indexed citations
10.
Tóth, Roland, et al.. (2024). Baseline Results for Selected Nonlinear System Identification Benchmarks. IFAC-PapersOnLine. 58(15). 474–479. 1 indexed citations
11.
Schoukens, Maarten, et al.. (2024). Locating nonlinearities in mechanical systems: A frequency-domain dynamic network perspective. Mechanical Systems and Signal Processing. 224. 112124–112124.
12.
Materassi, Donatello, Sean Warnick, Cristian Rojas, Maarten Schoukens, & Elizabeth J. Cross. (2024). Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part II). IFAC-PapersOnLine. 58(15). 497–501. 2 indexed citations
13.
Breschi, Valentina, et al.. (2024). Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees. TU/e Research Portal. 140–145. 2 indexed citations
14.
Schoukens, Maarten, et al.. (2023). Automated multi-objective system identification using grammar-based genetic programming. Automatica. 154. 111017–111017. 3 indexed citations
15.
Schoukens, Maarten, et al.. (2023). A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies. IFAC-PapersOnLine. 56(2). 2798–2804. 2 indexed citations
16.
Forgione, Marco, et al.. (2022). NARX Identification using Derivative-Based Regularized Neural Networks. 2022 IEEE 61st Conference on Decision and Control (CDC). 1515–1520. 1 indexed citations
17.
Haesaert, Sofie, et al.. (2022). Deep-Learning-Based Identification of LPV Models for Nonlinear Systems. 2022 IEEE 61st Conference on Decision and Control (CDC). 3274–3280. 5 indexed citations
18.
Schoukens, Maarten, et al.. (2021). Data-driven modeling of impedance biosensors: a subspace approach. Measurement Science and Technology. 32(10). 104009–104009. 4 indexed citations
19.
Schoukens, Maarten, et al.. (2021). Nonlinear Finite Impulse Response Estimation using Regularized Neural Networks. IFAC-PapersOnLine. 54(7). 174–179. 4 indexed citations
20.
Rébillat, Marc & Maarten Schoukens. (2017). Comparison of least squares and exponential sine sweep methods for Parallel Hammerstein Models estimation. Mechanical Systems and Signal Processing. 104. 851–865. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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