John Jakeman

3.1k total citations
57 papers, 1.2k citations indexed

About

John Jakeman is a scholar working on Statistics, Probability and Uncertainty, Civil and Structural Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, John Jakeman has authored 57 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Statistics, Probability and Uncertainty, 14 papers in Civil and Structural Engineering and 13 papers in Statistical and Nonlinear Physics. Recurrent topics in John Jakeman's work include Probabilistic and Robust Engineering Design (39 papers), Advanced Multi-Objective Optimization Algorithms (12 papers) and Structural Health Monitoring Techniques (12 papers). John Jakeman is often cited by papers focused on Probabilistic and Robust Engineering Design (39 papers), Advanced Multi-Objective Optimization Algorithms (12 papers) and Structural Health Monitoring Techniques (12 papers). John Jakeman collaborates with scholars based in United States, Australia and Italy. John Jakeman's co-authors include Michael Eldred, Dongbin Xiu, Akil Narayan, Alex Gorodetsky, Gianluca Geraci, Khachik Sargsyan, Tao Tang, Timothy Wildey, Troy Butler and Tong Qin and has published in prestigious journals such as Water Resources Research, Journal of Computational Physics and IEEE Access.

In The Last Decade

John Jakeman

56 papers receiving 1.1k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
John Jakeman 664 258 253 224 194 57 1.2k
Bertrand Iooss 1.2k 1.8× 221 0.9× 538 2.1× 451 2.0× 303 1.6× 81 2.5k
Sergey Oladyshkin 591 0.9× 131 0.5× 174 0.7× 262 1.2× 486 2.5× 52 1.2k
John A. Cafeo 489 0.7× 111 0.4× 356 1.4× 119 0.5× 113 0.6× 28 1.0k
Thierry A. Mara 785 1.2× 61 0.2× 154 0.6× 466 2.1× 509 2.6× 44 1.5k
Amandine Marrel 509 0.8× 82 0.3× 274 1.1× 148 0.7× 132 0.7× 38 934
Stefano Marelli 1.4k 2.1× 185 0.7× 479 1.9× 913 4.1× 265 1.4× 80 2.7k
Cedric Jean-Marie Sallaberry 1.1k 1.7× 45 0.2× 240 0.9× 349 1.6× 323 1.7× 41 1.9k
Ahmed H. Elsheikh 189 0.3× 203 0.8× 97 0.4× 101 0.5× 362 1.9× 87 1.5k
Charles Tong 213 0.3× 59 0.2× 210 0.8× 163 0.7× 325 1.7× 37 1.3k
Clémentine Prieur 330 0.5× 89 0.3× 105 0.4× 87 0.4× 100 0.5× 61 1.1k

Countries citing papers authored by John Jakeman

Since Specialization
Citations

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

Fields of papers citing papers by John Jakeman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Jakeman

This figure shows the co-authorship network connecting the top 25 collaborators of John Jakeman. A scholar is included among the top collaborators of John Jakeman 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 John Jakeman. John Jakeman 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.
Zeng, Xiaoshu, Gianluca Geraci, Alex Gorodetsky, John Jakeman, & Roger Ghanem. (2025). Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks. Computer Methods in Applied Mechanics and Engineering. 436. 117657–117657. 2 indexed citations
2.
Hoffman, Matthew J., et al.. (2024). Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt. ˜The œcryosphere. 18(11). 5207–5238. 1 indexed citations
3.
4.
Zeng, Xiaoshu, Gianluca Geraci, Michael Eldred, et al.. (2023). Multifidelity uncertainty quantification with models based on dissimilar parameters. Computer Methods in Applied Mechanics and Engineering. 415. 116205–116205. 15 indexed citations
5.
Kadeethum, Teeratorn, John Jakeman, Youngsoo Choi, Nikolaos Bouklas, & Hongkyu Yoon. (2023). Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. IEEE Access. 11. 62970–62985. 2 indexed citations
6.
Jakeman, John. (2022). PyApprox: Approximation and Probabilistic Analysis of Data.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2 indexed citations
7.
Kouri, Drew, et al.. (2022). Risk-Adapted Optimal Experimental Design. SIAM/ASA Journal on Uncertainty Quantification. 10(2). 687–716. 3 indexed citations
8.
Jakeman, John, et al.. (2022). Adaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems. International Journal for Numerical Methods in Engineering. 123(12). 2760–2790. 9 indexed citations
9.
Jakeman, John, et al.. (2022). Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk. Reliability Engineering & System Safety. 221. 108280–108280. 10 indexed citations
10.
Jakeman, John, et al.. (2021). MFNETS: Multi-Fidelity Data-Driven Networks for Data Analysis.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
11.
Butler, Troy, John Jakeman, & Timothy Wildey. (2020). Optimal experimental design for prediction based on push-forward probability measures. Journal of Computational Physics. 416. 109518–109518. 7 indexed citations
12.
Fu, Baihua, Jeffery S. Horsburgh, Anthony J. Jakeman, et al.. (2020). Modeling Water Quality in Watersheds: From Here to the Next Generation. Water Resources Research. 56(11). 68 indexed citations
13.
Gorodetsky, Alex, Gianluca Geraci, Michael Eldred, & John Jakeman. (2020). A generalized approximate control variate framework for multifidelity uncertainty quantification. Journal of Computational Physics. 408. 109257–109257. 75 indexed citations
14.
Jakeman, John, et al.. (2019). Polynomial chaos expansions for dependent random variables. Computer Methods in Applied Mechanics and Engineering. 351. 643–666. 59 indexed citations
15.
Jakeman, John, Michael Eldred, Gianluca Geraci, & Alex Gorodetsky. (2019). Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis. International Journal for Numerical Methods in Engineering. 121(6). 1314–1343. 30 indexed citations
16.
Jakeman, John & Akil Narayan. (2018). Generation and application of multivariate polynomial quadrature rules. Computer Methods in Applied Mechanics and Engineering. 338. 134–161. 12 indexed citations
17.
Gorodetsky, Alex & John Jakeman. (2018). Gradient-based optimization for regression in the functional tensor-train format. Journal of Computational Physics. 374. 1219–1238. 23 indexed citations
18.
Jakeman, John, Michael Eldred, & Khachik Sargsyan. (2015). Enhancingℓ1-minimization estimates of polynomial chaos expansions using basis selection. Journal of Computational Physics. 289. 18–34. 117 indexed citations
19.
Jakeman, John & Timothy Wildey. (2014). Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates. Journal of Computational Physics. 280. 54–71. 11 indexed citations
20.
Jakeman, John. (2007). Developments in integrated environmental assessment. Elsevier eBooks. 23 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Bertrand Iooss Breakdown of academic impact, for papers by Sergey Oladyshkin Breakdown of academic impact, for papers by John A. Cafeo Breakdown of academic impact, for papers by Thierry A. Mara Breakdown of academic impact, for papers by Amandine Marrel Breakdown of academic impact, for papers by Stefano Marelli Breakdown of academic impact, for papers by Cedric Jean-Marie Sallaberry Breakdown of academic impact, for papers by Ahmed H. Elsheikh Breakdown of academic impact, for papers by Charles Tong Breakdown of academic impact, for papers by Clémentine Prieur
Rankless by CCL
2026