Adam Thelen

1.3k total citations · 2 hit papers
23 papers, 843 citations indexed

About

Adam Thelen is a scholar working on Automotive Engineering, Electrical and Electronic Engineering and Control and Systems Engineering. According to data from OpenAlex, Adam Thelen has authored 23 papers receiving a total of 843 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Automotive Engineering, 14 papers in Electrical and Electronic Engineering and 7 papers in Control and Systems Engineering. Recurrent topics in Adam Thelen's work include Advanced Battery Technologies Research (14 papers), Advancements in Battery Materials (11 papers) and Digital Transformation in Industry (5 papers). Adam Thelen is often cited by papers focused on Advanced Battery Technologies Research (14 papers), Advancements in Battery Materials (11 papers) and Digital Transformation in Industry (5 papers). Adam Thelen collaborates with scholars based in United States, Switzerland and Hong Kong. Adam Thelen's co-authors include Chao Hu, Olga Fink, Sheng Shen, Michael D. Todd, Byeng D. Youn, Xiaoge Zhang, Sayan Ghosh, Sankaran Mahadevan, Yan Lu and Zhen Hu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Power Sources and Applied Energy.

In The Last Decade

Adam Thelen

22 papers receiving 817 citations

Hit Papers

A comprehensive review of digital twin — part 1: modeling... 2021 2026 2022 2024 2022 2021 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
    2022 208 citations Adam Thelen, Xiaoge Zhang et al. Structural and Multidisciplinary Optimization profile →
  2. A physics-informed deep learning approach for bearing fault detection
    2021 184 citations Sheng Shen, Hao Lü et al. Engineering Applications of Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Adam Thelen United States 11 273 266 254 171 153 23 843
Wei Shangguan China 16 418 1.5× 319 1.2× 215 0.8× 146 0.9× 111 0.7× 145 968
Diyin Tang China 17 324 1.2× 210 0.8× 291 1.1× 187 1.1× 179 1.2× 52 987
Laifa Tao China 18 547 2.0× 416 1.6× 397 1.6× 207 1.2× 56 0.4× 62 1.1k
Jilun Tian China 12 531 1.9× 128 0.5× 143 0.6× 178 1.0× 48 0.3× 23 908
Shaowei Chen China 15 288 1.1× 148 0.6× 218 0.9× 138 0.8× 37 0.2× 53 959
R. Jegadeeshwaran India 19 377 1.4× 103 0.4× 256 1.0× 635 3.7× 248 1.6× 76 1.1k
Zhimin Xi United States 14 111 0.4× 292 1.1× 158 0.6× 181 1.1× 61 0.4× 41 660
Bing Long China 19 525 1.9× 391 1.5× 725 2.9× 74 0.4× 121 0.8× 71 1.2k

Countries citing papers authored by Adam Thelen

Since Specialization
Citations

This map shows the geographic impact of Adam Thelen'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 Adam Thelen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam Thelen more than expected).

Fields of papers citing papers by Adam Thelen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adam Thelen. 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 Adam Thelen. The network helps show where Adam Thelen may publish in the future.

Co-authorship network of co-authors of Adam Thelen

This figure shows the co-authorship network connecting the top 25 collaborators of Adam Thelen. A scholar is included among the top collaborators of Adam Thelen 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 Adam Thelen. Adam Thelen 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.
2.
Ramamurthy, Jayanth R., et al.. (2024). Rapid estimation of lithium-ion battery capacity and resistances from short duration current pulses. Journal of Power Sources. 628. 235813–235813. 8 indexed citations
3.
Thelen, Adam, et al.. (2024). Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods. Energy storage materials. 68. 103343–103343. 37 indexed citations
4.
Zhou, Zihao, et al.. (2024). Predicting battery lifetime under varying usage conditions from early aging data. Cell Reports Physical Science. 5(4). 101891–101891. 28 indexed citations
5.
Lü, Hao, Adam Thelen, Olga Fink, Chao Hu, & Simon Laflamme. (2024). Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis. Mechanical Systems and Signal Processing. 210. 111068–111068. 11 indexed citations
6.
Thelen, Adam, Xun Huan, Noah H. Paulson, et al.. (2024). Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives. SHILAP Revista de lepidopterología. 2(1). 32 indexed citations
7.
Thelen, Adam, Xiaoge Zhang, Olga Fink, et al.. (2023). Correction: A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives. Structural and Multidisciplinary Optimization. 66(1). 10 indexed citations
8.
Thelen, Adam, Murtaza Zohair, Jayanth R. Ramamurthy, et al.. (2023). Sequential Bayesian optimization for accelerating the design of sodium metal battery nucleation layers. Journal of Power Sources. 581. 233508–233508. 6 indexed citations
9.
10.
Thelen, Adam, Yu Hui Lui, Sheng Shen, et al.. (2022). Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries. Energy storage materials. 50. 668–695. 97 indexed citations
11.
Nelson, Matthew J., Simon Laflamme, Chao Hu, et al.. (2022). Multi-step ahead state estimation with hybrid algorithm for high-rate dynamic systems. Mechanical Systems and Signal Processing. 182. 109536–109536. 6 indexed citations
12.
Thelen, Adam, Xiaoge Zhang, Olga Fink, et al.. (2022). A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives. Structural and Multidisciplinary Optimization. 66(1). 94 indexed citations
13.
Thelen, Adam, Xiaoge Zhang, Olga Fink, et al.. (2022). A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies. arXiv (Cornell University). 4 indexed citations
14.
Nemani, Venkat Pavan, et al.. (2022). Dynamically Weighted Ensemble of Diverse Learners for Remaining Useful Life Prediction. 2 indexed citations
15.
Nemani, Venkat Pavan, et al.. (2022). Health index construction with feature fusion optimization for predictive maintenance of physical systems. Structural and Multidisciplinary Optimization. 65(12). 4 indexed citations
16.
Thelen, Adam, Meng Li, Chao Hu, et al.. (2022). Augmented model-based framework for battery remaining useful life prediction. Applied Energy. 324. 119624–119624. 57 indexed citations
17.
Shen, Sheng, Hao Lü, Mohammadkazem Sadoughi, et al.. (2021). A physics-informed deep learning approach for bearing fault detection. Engineering Applications of Artificial Intelligence. 103. 104295–104295. 184 indexed citations breakdown →
18.
Thelen, Adam, Yu Hui Lui, Sheng Shen, et al.. (2021). Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries. 3 indexed citations
19.
Liu, Jinqiang, et al.. (2021). An End-to-End Learning Framework for Battery Capacity-Fade Trajectory Prediction Using Early Life Data. Annual Conference of the PHM Society. 13(1). 3 indexed citations
20.
Nemani, Venkat Pavan, Hao Lü, Adam Thelen, Chao Hu, & Andrew T. Zimmerman. (2021). Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards. Neurocomputing. 491. 575–596. 38 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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