Koen Tiels: h-index, Total Citations, and Citation Map
Koen Tiels's h-index is 19 (32 i10-index, 1,795+ total citations across 5+ publications) according to Google Scholar as of May 2026. Koen Tiels is affiliated with Lecturer, Eindhoven University of Technology (TU/e).
Koen Tiels is a researcher affiliated with Lecturer, Eindhoven University of Technology (TU/e), specializing in system identification, data-driven modeling. Their work has been cited 1,795 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Koen Tiels's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 19
- i10-Index
- 32
- Total Citations
- 1,795
- Citing Countries
- 19
As of May 2026.
Koen Tiels has an h-index of 19 and 1,795 total citations across 5 publications, with research cited by institutions in 19 countries.
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Global Impact Map
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Top Cited Works
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Identification of block-oriented nonlinear systems starting from linear approximations: A survey
2017321
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher pioneered the application of deep convolutional networks to system identification, establishing a foundational framework that subsequent work expanded into broader deep learning methodologies for dynamic system modeling.
The researcher advanced RNN training theory by analyzing stability through attractors and smoothness, offering a framework beyond gradient issues.
The researcher established a foundational survey framework for identifying block-oriented nonlinear systems by leveraging linear approximations, a seminal contribution published in Automatica that has garnered significant independent scholarly attention.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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