Mengwu Guo

1.1k total citations
22 papers, 746 citations indexed

About

Mengwu Guo is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and Civil and Structural Engineering. According to data from OpenAlex, Mengwu Guo has authored 22 papers receiving a total of 746 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Statistical and Nonlinear Physics, 15 papers in Statistics, Probability and Uncertainty and 7 papers in Civil and Structural Engineering. Recurrent topics in Mengwu Guo's work include Model Reduction and Neural Networks (17 papers), Probabilistic and Robust Engineering Design (15 papers) and Structural Health Monitoring Techniques (5 papers). Mengwu Guo is often cited by papers focused on Model Reduction and Neural Networks (17 papers), Probabilistic and Robust Engineering Design (15 papers) and Structural Health Monitoring Techniques (5 papers). Mengwu Guo collaborates with scholars based in Netherlands, China and Switzerland. Mengwu Guo's co-authors include Jan S. Hesthaven, Andrea Manzoni, Chao Yan, Jian Yu, Ehsan Haghighat, Karen Willcox, Steven L. Brunton, J. Nathan Kutz, Hongzhi Zhong and Paolo Zunino and has published in prestigious journals such as Scientific Reports, Computer Methods in Applied Mechanics and Engineering and Journal of Engineering Mechanics.

In The Last Decade

Mengwu Guo

21 papers receiving 735 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mengwu Guo Netherlands 11 477 279 213 143 123 22 746
Matthew J. Zahr United States 17 775 1.6× 343 1.2× 570 2.7× 122 0.9× 132 1.1× 45 1.1k
David J. Knezevic United States 17 409 0.9× 212 0.8× 312 1.5× 97 0.7× 49 0.4× 33 821
Julien Cortial France 8 820 1.7× 401 1.4× 499 2.3× 188 1.3× 101 0.8× 16 1.1k
Laura Mainini Italy 12 204 0.4× 168 0.6× 117 0.5× 97 0.7× 84 0.7× 36 560
Francesco Ballarin Italy 17 618 1.3× 199 0.7× 524 2.5× 47 0.3× 73 0.6× 54 909
Rohit Tripathy United States 5 243 0.5× 251 0.9× 89 0.4× 80 0.6× 47 0.4× 7 584
M. Damodaran Singapore 11 315 0.7× 177 0.6× 386 1.8× 40 0.3× 204 1.7× 51 694
Apostolos F. Psaros United States 13 255 0.5× 315 1.1× 77 0.4× 186 1.3× 57 0.5× 17 675
P. Astrid Netherlands 10 415 0.9× 186 0.7× 187 0.9× 116 0.8× 35 0.3× 17 619
Tingwei Ji China 11 224 0.5× 80 0.3× 313 1.5× 61 0.4× 216 1.8× 32 569

Countries citing papers authored by Mengwu Guo

Since Specialization
Citations

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

Fields of papers citing papers by Mengwu Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mengwu Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Mengwu Guo. A scholar is included among the top collaborators of Mengwu Guo 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 Mengwu Guo. Mengwu Guo 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.
Du, Xiaosong, Mengwu Guo, Joaquim R. R. A. Martins, et al.. (2025). Improving Neural Network Efficiency With Multifidelity and Dimensionality Reduction Techniques. Lund University Publications (Lund University). 2 indexed citations
2.
Brüne, Christoph, et al.. (2025). PDE-constrained Gaussian process surrogate modeling with uncertain data locations. Advanced Modeling and Simulation in Engineering Sciences. 12(1).
3.
Guo, Mengwu, et al.. (2024). Multi-fidelity reduced-order surrogate modelling. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 480(2283). 17 indexed citations
4.
Guo, Mengwu, et al.. (2024). Gaussian process learning of nonlinear dynamics. Communications in Nonlinear Science and Numerical Simulation. 138. 108184–108184. 2 indexed citations
5.
Xie, Xiang, Wei Wang, Haijun Wu, & Mengwu Guo. (2023). Data-driven analysis of parametrized acoustic systems in the frequency domain. Applied Mathematical Modelling. 124. 791–805. 1 indexed citations
6.
Fresca, Stefania, et al.. (2023). Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. Computers & Mathematics with Applications. 149. 1–23. 17 indexed citations
7.
Guo, Mengwu & Ehsan Haghighat. (2022). Energy-Based Error Bound of Physics-Informed Neural Network Solutions in Elasticity. Journal of Engineering Mechanics. 148(8). 22 indexed citations
8.
Guo, Mengwu, et al.. (2022). Multi-fidelity surrogate modeling using long short-term memory networks. Computer Methods in Applied Mechanics and Engineering. 404. 115811–115811. 49 indexed citations
9.
Guo, Mengwu, et al.. (2022). Deep kernel learning of dynamical models from high-dimensional noisy data. Scientific Reports. 12(1). 21530–21530. 7 indexed citations
10.
Guo, Mengwu, et al.. (2022). Bayesian operator inference for data-driven reduced-order modeling. Computer Methods in Applied Mechanics and Engineering. 402. 115336–115336. 33 indexed citations
11.
Guo, Mengwu, et al.. (2021). Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities. Computer Methods in Applied Mechanics and Engineering. 389. 114378–114378. 87 indexed citations
12.
Yu, Jian, Chao Yan, & Mengwu Guo. (2019). Non-intrusive reduced-order modeling for fluid problems: A brief review. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering. 233(16). 5896–5912. 76 indexed citations
13.
Guo, Mengwu, et al.. (2019). Model order reduction for large-scale structures with local nonlinearities. Computer Methods in Applied Mechanics and Engineering. 353. 491–515. 11 indexed citations
14.
Guo, Mengwu & Jan S. Hesthaven. (2018). Reduced order modeling for nonlinear structural analysis using Gaussian process regression. Computer Methods in Applied Mechanics and Engineering. 341. 807–826. 194 indexed citations
15.
Guo, Mengwu & Jan S. Hesthaven. (2018). Data-driven reduced order modeling for time-dependent problems. Computer Methods in Applied Mechanics and Engineering. 345. 75–99. 173 indexed citations
16.
Guo, Mengwu & Hongzhi Zhong. (2017). Strict upper and lower bounds for quantities of interest in static response sensitivity analysis. Applied Mathematical Modelling. 49. 17–34. 3 indexed citations
17.
Guo, Mengwu, et al.. (2016). Identification of imperfections in thin plates based on the modified potential energy principle. Mechanics Research Communications. 72. 16–23. 1 indexed citations
18.
Guo, Mengwu, et al.. (2016). A second-order perturbation method for fuzzy eigenvalue problems. Engineering Computations. 33(2). 3 indexed citations
19.
Guo, Mengwu, et al.. (2015). Goal-oriented error estimation for beams on elastic foundation with double shear effect. Applied Mathematical Modelling. 39(16). 4699–4714. 2 indexed citations
20.
Wang, Li, Mengwu Guo, & Hongzhi Zhong. (2015). Strict upper and lower bounds of quantities for beams on elastic foundation by dual analysis. Engineering Computations. 32(6). 1619–1642. 1 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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