Rémi Lam

2.8k total citations · 3 hit papers
15 papers, 1.4k citations indexed

About

Rémi Lam is a scholar working on Computational Theory and Mathematics, Statistics, Probability and Uncertainty and Computational Mechanics. According to data from OpenAlex, Rémi Lam has authored 15 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Theory and Mathematics, 7 papers in Statistics, Probability and Uncertainty and 4 papers in Computational Mechanics. Recurrent topics in Rémi Lam's work include Advanced Multi-Objective Optimization Algorithms (7 papers), Probabilistic and Robust Engineering Design (7 papers) and Meteorological Phenomena and Simulations (3 papers). Rémi Lam is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (7 papers), Probabilistic and Robust Engineering Design (7 papers) and Meteorological Phenomena and Simulations (3 papers). Rémi Lam collaborates with scholars based in United States, United Kingdom and Portugal. Rémi Lam's co-authors include Karen Willcox, Shakir Mohamed, Suman Ravuri, Álvaro Sánchez‐González, Timo Ewalds, Ferran Alet, Peter Battaglia, Weihua Hu, Peter Wirnsberger and Stephan Hoyer and has published in prestigious journals such as Nature, Science and AIAA Journal.

In The Last Decade

Rémi Lam

15 papers receiving 1.4k 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.

Skilful precipitation nowcasting using deep generative models of radar

2021 554 citations Suman Ravuri, Karel Lenc et al. Nature profile →
Learning skillful medium-range global weather forecasting

2023 535 citations Rémi Lam, Álvaro Sánchez‐González et al. Science profile →
Probabilistic weather forecasting with machine learning

2024 96 citations Álvaro Sánchez‐González, Ferran Alet et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rémi Lam United States	10	670	534	276	248	154	15	1.4k
Xiaodong Luo Norway	26	345 0.5×	252 0.5×	203 0.7×	164 0.7×	134 0.9×	134	1.9k
Emil M. Constantinescu United States	22	513 0.8×	352 0.7×	199 0.7×	181 0.7×	89 0.6×	78	1.9k
Haroldo Fraga de Campos Velho Brazil	20	333 0.5×	391 0.7×	530 1.9×	140 0.6×	53 0.3×	163	1.6k
Nan Chen United States	28	1.0k 1.5×	735 1.4×	245 0.9×	94 0.4×	32 0.2×	138	2.2k
Rossella Arcucci United Kingdom	22	449 0.7×	302 0.6×	246 0.9×	270 1.1×	28 0.2×	87	1.5k
Matthias Katzfuß United States	15	264 0.4×	405 0.8×	539 2.0×	405 1.6×	37 0.2×	41	1.5k
Michael Scheuerer United States	19	875 1.3×	857 1.6×	266 1.0×	107 0.4×	34 0.2×	39	1.3k
Andrew Zammit‐Mangion Australia	15	339 0.5×	338 0.6×	351 1.3×	223 0.9×	33 0.2×	66	1.4k
Jochen Bröcker United Kingdom	12	375 0.6×	372 0.7×	96 0.3×	121 0.5×	19 0.1×	43	740
Caren Marzban United States	21	734 1.1×	633 1.2×	347 1.3×	164 0.7×	13 0.1×	49	1.3k

Countries citing papers authored by Rémi Lam

Since Specialization

Citations

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

Fields of papers citing papers by Rémi Lam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rémi Lam

This figure shows the co-authorship network connecting the top 25 collaborators of Rémi Lam. A scholar is included among the top collaborators of Rémi Lam 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 Rémi Lam. Rémi Lam 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:

15 of 15 papers shown

1.

Sánchez‐González, Álvaro, Ferran Alet, Tom R. Andersson, et al.. (2024). Probabilistic weather forecasting with machine learning. Nature. 637(8044). 84–90. 96 indexed citations breakdown →

2.

Lam, Rémi, Álvaro Sánchez‐González, Peter Wirnsberger, et al.. (2023). Learning skillful medium-range global weather forecasting. Science. 382(6677). 1416–1421. 535 indexed citations breakdown →

3.

Ravuri, Suman, Karel Lenc, Dmitry Kangin, et al.. (2021). Skilful precipitation nowcasting using deep generative models of radar. Nature. 597(7878). 672–677. 554 indexed citations breakdown →

4.

Lam, Rémi, et al.. (2020). Multifidelity Method for Locating Aeroelastic Flutter Boundaries. AIAA Journal. 58(4). 1772–1784. 14 indexed citations

5.

Lam, Rémi, et al.. (2020). Multifidelity Dimension Reduction via Active Subspaces. SIAM Journal on Scientific Computing. 42(2). A929–A956. 28 indexed citations

6.

Chaudhuri, Anirban, et al.. (2019). Reusing Information for Multifidelity Active Learning in Reliability-Based Design Optimization. AIAA Scitech 2019 Forum. 7 indexed citations

7.

Lam, Rémi, et al.. (2019). A multifidelity method for locating aeroelastic flutter boundaries. AIAA Scitech 2019 Forum. 4 indexed citations

8.

Lam, Rémi, et al.. (2018). Contour location via entropy reduction leveraging multiple information sources. Neural Information Processing Systems. 31. 5217–5227. 2 indexed citations

9.

Lam, Rémi, Matthias Poloczek, Peter I. Frazier, & Karen Willcox. (2018). Advances in Bayesian Optimization with Applications in Aerospace Engineering. DSpace@MIT (Massachusetts Institute of Technology). 39 indexed citations

10.

Lam, Rémi & Karen Willcox. (2017). Lookahead Bayesian Optimization with Inequality Constraints. Neural Information Processing Systems. 30. 1890–1900. 14 indexed citations

11.

Chaudhuri, Anirban, Rémi Lam, & Karen Willcox. (2017). Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems. AIAA Journal. 56(1). 235–249. 41 indexed citations

12.

Lam, Rémi, Karen Willcox, & David H. Wolpert. (2016). Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach. Neural Information Processing Systems. 29. 883–891. 29 indexed citations

13.

Lam, Rémi, Douglas Allaire, & Karen Willcox. (2015). Multifidelity Optimization using Statistical Surrogate Modeling for Non-Hierarchical Information Sources. 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 55 indexed citations

14.

Saatdjian, E., Rémi Lam, & José P. B. Mota. (1999). Natural convection heat transfer in the annular region between porous confocal ellipses. International Journal for Numerical Methods in Fluids. 31(2). 513–522. 1 indexed citations

15.

Saatdjian, E., Rémi Lam, & José P. B. Mota. (1999). Natural convection heat transfer in the annular region between porous confocal ellipses. International Journal for Numerical Methods in Fluids. 31(2). 513–522. 8 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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