Chenxi Wu

978 total citations · 2 hit papers
27 papers, 577 citations indexed

About

Chenxi Wu is a scholar working on Control and Systems Engineering, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Chenxi Wu has authored 27 papers receiving a total of 577 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Control and Systems Engineering, 8 papers in Signal Processing and 6 papers in Artificial Intelligence. Recurrent topics in Chenxi Wu's work include Direction-of-Arrival Estimation Techniques (7 papers), Speech and Audio Processing (7 papers) and Machine Fault Diagnosis Techniques (7 papers). Chenxi Wu is often cited by papers focused on Direction-of-Arrival Estimation Techniques (7 papers), Speech and Audio Processing (7 papers) and Machine Fault Diagnosis Techniques (7 papers). Chenxi Wu collaborates with scholars based in China and United States. Chenxi Wu's co-authors include Lu Lu, Min Zhu, Tefang Chen, George Em Karniadakis, Pengfei Li, Juan Diego Toscano, Min Zhang, Rong Jiang, Zheng Jiang and Zongren Zou and has published in prestigious journals such as IEEE Access, Computer Methods in Applied Mechanics and Engineering and Sensors.

In The Last Decade

Chenxi Wu

23 papers receiving 556 citations

Hit Papers

align trajectories

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.

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

2022 319 citations Chenxi Wu, Min Zhu et al. Computer Methods in Applied Mechanics and Engineering profile →
From PINNs to PIKANs: recent advances in physics-informed machine learning

2025 60 citations Juan Diego Toscano, Vivek Oommen et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chenxi Wu China	9	267	127	113	92	88	27	577
Eurika Kaiser United States	10	529 2.0×	355 2.8×	177 1.6×	55 0.6×	139 1.6×	25	912
Zhiwen Zhu Canada	17	219 0.8×	38 0.3×	77 0.7×	41 0.4×	134 1.5×	62	635
Suraj Pawar United States	13	287 1.1×	246 1.9×	106 0.9×	62 0.7×	66 0.8×	34	556
Zongren Zou United States	10	297 1.1×	73 0.6×	84 0.7×	50 0.5×	160 1.8×	15	588
Kumi Nakai Japan	13	79 0.3×	110 0.9×	101 0.9×	40 0.4×	101 1.1×	35	382
Apostolos F. Psaros United States	13	255 1.0×	77 0.6×	57 0.5×	70 0.8×	90 1.0×	17	675
Kookjin Lee United States	9	302 1.1×	161 1.3×	60 0.5×	38 0.4×	72 0.8×	26	462
Susmita Mall India	11	309 1.2×	86 0.7×	18 0.2×	50 0.5×	171 1.9×	13	586
Youngsoo Choi United States	16	581 2.2×	317 2.5×	104 0.9×	89 1.0×	71 0.8×	35	809

Countries citing papers authored by Chenxi Wu

Since Specialization

Citations

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

Fields of papers citing papers by Chenxi Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenxi Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Chenxi Wu. A scholar is included among the top collaborators of Chenxi Wu 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 Chenxi Wu. Chenxi Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Wu, Chenxi, et al.. (2025). Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning. SIAM Journal on Scientific Computing. 47(3). C559–C577.

2.

Wu, Chenxi, Juan Diego Toscano, Yingjie Chen, et al.. (2025). FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design. Chemical Engineering Science. 320. 122348–122348.

3.

Toscano, Juan Diego, et al.. (2025). From PINNs to PIKANs: recent advances in physics-informed machine learning. 1(1). 60 indexed citations breakdown →

4.

Toscano, Juan Diego, Chenxi Wu, Antonio Ladrón-de-Guevara, et al.. (2024). Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification. Interface Focus. 14(6). 20240030–20240030. 5 indexed citations

5.

Wu, Chenxi, et al.. (2024). GPT VS. HUMAN FOR SCIENTIFIC REVIEWS: A DUAL SOURCE REVIEW ON APPLICATIONS OF CHATGPT IN SCIENCE. 5(2). 1–44. 2 indexed citations

6.

Wu, Chenxi, et al.. (2024). A depthwise separable convolution-based neural network for rolling bearing fault diagnosis. Journal of Computational Methods in Sciences and Engineering. 24(6). 4153–4170. 1 indexed citations

7.

Tiozzo, Giulio, et al.. (2024). Master teapots and entropy algorithms for the Mandelbrot set. Transactions of the American Mathematical Society. 378(5). 3297–3348.

8.

Zhang, Min, et al.. (2023). BD-CVSA: A Broadband Direction Finding Method Based on Constructing Virtual Sparse Arrays. Signal Processing. 212. 109160–109160.

9.

Wu, Chenxi, et al.. (2023). A Time–Frequency Residual Convolution Neural Network for the Fault Diagnosis of Rolling Bearings. Processes. 12(1). 54–54. 4 indexed citations

10.

Wu, Chenxi, et al.. (2022). A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering. 403. 115671–115671. 319 indexed citations breakdown →

11.

Wu, Chenxi, et al.. (2020). Array Diagnosis and DOA Estimation for Coprime Array under Sensor Failures. Sensors. 20(9). 2735–2735. 2 indexed citations

12.

Zhang, Min, et al.. (2020). Two-Dimensional DOA Estimation via Deep Ensemble Learning. IEEE Access. 8. 124544–124552. 34 indexed citations

13.

Wu, Chenxi, et al.. (2020). Direction-of-Arrival Estimation for Coprime Array via Covariance Matrix Reconstruction. 634–638. 3 indexed citations

14.

Wu, Chenxi, et al.. (2019). Direction-of-Arrival Estimation Under Array Sensor Failures with ULA. IEEE Access. 8. 26445–26456. 17 indexed citations

15.

Zhang, Min, et al.. (2019). Broadband Direction of Arrival Estimation Based on Convolutional Neural Network. IEICE Transactions on Communications. E103.B(3). 148–154. 11 indexed citations

16.

Wu, Chenxi, et al.. (2019). Side Channel Attack of Lightweight Block Cipher Simeck Based on Deep Learning. 1–5. 1 indexed citations

17.

Wu, Chenxi, et al.. (2017). Bearing fault diagnosis via kernel matrix construction based support vector machine. Journal of Vibroengineering. 19(5). 3445–3461. 12 indexed citations

18.

Wu, Chenxi, et al.. (2017). Underdetermined DOA estimation based on covariance matrix reconstruction using coprime array. 99. 671–674. 2 indexed citations

19.

Wu, Chenxi, et al.. (2015). ANN Based Multi-classification Using Various Signal Processing Techniques for Bearing Fault Diagnosis. International Journal of Control and Automation. 8(7). 113–124. 11 indexed citations

20.

Wu, Chenxi, et al.. (2013). Intelligent Identification of Bearing Faults Using Time Domain Features. 713–716. 7 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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