Andrew M. Stuart

19.4k total citations · 3 hit papers
208 papers, 9.7k citations indexed

About

Andrew M. Stuart is a scholar working on Artificial Intelligence, Statistics and Probability and Statistical and Nonlinear Physics. According to data from OpenAlex, Andrew M. Stuart has authored 208 papers receiving a total of 9.7k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Artificial Intelligence, 55 papers in Statistics and Probability and 38 papers in Statistical and Nonlinear Physics. Recurrent topics in Andrew M. Stuart's work include Markov Chains and Monte Carlo Methods (48 papers), Gaussian Processes and Bayesian Inference (47 papers) and Probabilistic and Robust Engineering Design (29 papers). Andrew M. Stuart is often cited by papers focused on Markov Chains and Monte Carlo Methods (48 papers), Gaussian Processes and Bayesian Inference (47 papers) and Probabilistic and Robust Engineering Design (29 papers). Andrew M. Stuart collaborates with scholars based in United Kingdom, United States and Spain. Andrew M. Stuart's co-authors include A. R. Humphries, Grigorios A. Pavliotis, Desmond J. Higham, Kody J. H. Law, Charles M. Elliott, Xuerong Mao, O. A. González, Gareth O. Roberts, Jonathan C. Mattingly and Marco Iglesias and has published in prestigious journals such as Cell, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Andrew M. Stuart

203 papers receiving 8.9k 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.

Inverse problems: A Bayesian perspective

2010 931 citations Andrew M. Stuart profile →
Multiscale Methods: Averaging and Homogenization

2008 449 citations Andrew M. Stuart et al. CaltechAUTHORS (California Institute of Technology) profile →
2012 333 citations Andrew M. Stuart et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Andrew M. Stuart United Kingdom	49	2.0k	1.8k	1.7k	1.6k	1.4k	208	9.7k
I. S. Gradshteyn United States	7	1.6k 0.8×	4.3k 2.3×	1.5k 0.9×	1.3k 0.8×	902 0.7×	8	32.0k
Alan Jeffrey United Kingdom	29	1.2k 0.6×	2.5k 1.4×	1.0k 0.6×	537 0.3×	742 0.5×	149	15.3k
I.M. RYZHIK United States	4	898 0.5×	2.4k 1.3×	995 0.6×	721 0.5×	574 0.4×	6	19.7k
Carl de Boor United States	41	1.0k 0.5×	614 0.3×	6.3k 3.8×	1.5k 1.0×	1.9k 1.4×	156	16.4k
Nicholas J. Higham United Kingdom	58	1.3k 0.7×	1.6k 0.9×	2.2k 1.3×	488 0.3×	7.7k 5.5×	266	14.5k
Alan Edelman United States	34	1.4k 0.7×	791 0.4×	1.2k 0.7×	1.1k 0.7×	1.3k 0.9×	122	9.7k
Cédric Villani France	38	1.1k 0.5×	1.7k 0.9×	1.4k 0.8×	1.2k 0.8×	1.4k 1.0×	82	11.6k
Peter E. Kloeden Germany	26	441 0.2×	946 0.5×	705 0.4×	423 0.3×	754 0.5×	64	6.0k
Lloyd N. Trefethen United Kingdom	54	723 0.4×	2.7k 1.5×	5.4k 3.2×	340 0.2×	3.9k 2.8×	175	17.4k
Richard H. Byrd United States	32	2.0k 1.0×	505 0.3×	1.8k 1.1×	477 0.3×	2.3k 1.7×	69	12.4k

Countries citing papers authored by Andrew M. Stuart

Since Specialization

Citations

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

Fields of papers citing papers by Andrew M. Stuart

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew M. Stuart. 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 Andrew M. Stuart. The network helps show where Andrew M. Stuart may publish in the future.

Co-authorship network of co-authors of Andrew M. Stuart

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew M. Stuart. A scholar is included among the top collaborators of Andrew M. Stuart 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 Andrew M. Stuart. Andrew M. Stuart 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

Wu, Jinlong, Matthew E. Levine, Tapio Schneider, & Andrew M. Stuart. (2024). Learning about structural errors in models of complex dynamical systems. Journal of Computational Physics. 513. 113157–113157. 10 indexed citations

Chen, Yifan, et al.. (2024). Error analysis of kernel/GP methods for nonlinear and parametric PDEs. Journal of Computational Physics. 520. 113488–113488. 4 indexed citations

Bhattacharya, Kaushik, et al.. (2024). Learning Homogenization for Elliptic Operators. SIAM Journal on Numerical Analysis. 62(4). 1844–1873. 3 indexed citations

Kim, Kyra H., Venkat Chandrasekaran, Zhen Liu, et al.. (2024). Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression. SHILAP Revista de lepidopterología. 1(4). 2 indexed citations

Hripcsak, George, et al.. (2024). A stochastic model-based control methodology for glycemic management in the intensive care unit. SHILAP Revista de lepidopterología. 2.

Levine, Matthew E., et al.. (2023). A simple modeling framework for prediction in the human glucose–insulin system. Chaos An Interdisciplinary Journal of Nonlinear Science. 33(7). 9 indexed citations

Liu, Burigede, et al.. (2023). Learning macroscopic internal variables and history dependence from microscopic models. Journal of the Mechanics and Physics of Solids. 178. 105329–105329. 27 indexed citations

Huang, Daniel Zhengyu, Jiaoyang Huang, Sebastian Reich, & Andrew M. Stuart. (2022). Efficient derivative-free Bayesian inference for large-scale inverse problems. Inverse Problems. 38(12). 125006–125006. 24 indexed citations

Garbuno-Iñigo, Alfredo, et al.. (2020). Calibrate, emulate, sample. Journal of Computational Physics. 424. 109716–109716. 67 indexed citations

10.

Li, Zongyi, Nikola Kovachki, Kamyar Azizzadenesheli, et al.. (2020). Multipole Graph Neural Operator for Parametric Partial Differential Equations. CaltechAUTHORS (California Institute of Technology). 33. 6755–6766. 8 indexed citations

11.

Kovachki, Nikola & Andrew M. Stuart. (2019). Ensemble Kalman inversion: a derivative-free technique for machine learning tasks. Inverse Problems. 35(9). 95005–95005. 73 indexed citations

12.

Albers, David J., et al.. (2019). Ensemble Kalman methods with constraints. Inverse Problems. 35(9). 95007–95007. 33 indexed citations

13.

Iglesias, Marco, et al.. (2018). Parameterizations for ensemble Kalman inversion. Inverse Problems. 34(5). 55009–55009. 48 indexed citations

14.

Girolami, Mark, et al.. (2018). How Deep Are Deep Gaussian Processes. Journal of Machine Learning Research. 19(54). 1–46. 51 indexed citations

15.

Lin, Kui, et al.. (2017). Filter based methods for statistical linear inverse problems. Warwick Research Archive Portal (University of Warwick). 12 indexed citations

16.

Lu, Yulong, Andrew M. Stuart, & Hendrik Weber. (2016). Gaussian approximations for transition paths in molecular dynamics. CaltechAUTHORS (California Institute of Technology). 2 indexed citations

17.

Hòang, Viêt Hà, Christoph Schwab, & Andrew M. Stuart. (2012). Sparse MCMC gpc Finite Element Methods for Bayesian Inverse Problems. arXiv (Cornell University). 3 indexed citations

18.

Pillai, Natesh S., Andrew M. Stuart, & Alexandre H. Thiéry. (2011). Optimal Proposal Design for Random Walk Type Metropolis Algorithms with Gaussian Random Field Priors. arXiv (Cornell University). 1 indexed citations

19.

Pillai, Natesh S., Andrew M. Stuart, & Alexandre H. Thiéry. (2011). On the random walk metropolis algorithm for Gaussian random field priors and the gradient flow. arXiv (Cornell University). 2 indexed citations

20.

Flandoli, Franco, Peter E. Kloeden, & Andrew M. Stuart. (2009). Infinite Dimensional Random Dynamical Systems and Their Applications. Oberwolfach Reports. 5(4). 2815–2874. 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.

Explore authors with similar magnitude of impact