Arthur Pajot

401 total citations
5 papers, 167 citations indexed

About

Arthur Pajot is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Atmospheric Science. According to data from OpenAlex, Arthur Pajot has authored 5 papers receiving a total of 167 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Statistical and Nonlinear Physics, 3 papers in Artificial Intelligence and 2 papers in Atmospheric Science. Recurrent topics in Arthur Pajot's work include Model Reduction and Neural Networks (3 papers), Meteorological Phenomena and Simulations (2 papers) and Neural Networks and Applications (1 paper). Arthur Pajot is often cited by papers focused on Model Reduction and Neural Networks (3 papers), Meteorological Phenomena and Simulations (2 papers) and Neural Networks and Applications (1 paper). Arthur Pajot collaborates with scholars based in France. Arthur Pajot's co-authors include Emmanuel de Bézenac, Patrick Gallinari and Patrick Gallinari and has published in prestigious journals such as Machine Learning, arXiv (Cornell University) and International Conference on Learning Representations.

In The Last Decade

Arthur Pajot

5 papers receiving 161 citations

Peers

Arthur Pajot

SNP

AS

GPC

EE

AI

Said Ouala France

Dragos B. Chirila Germany

J. Brandstetter Austria

Javier González-Rocha United States

Zhiqiang Gong China

Mariana Clare United Kingdom

Sasan Tavakkol United States

Lucas Drumetz France

Fred W. Bacon United States

N. Kourti Italy

Said Ouala France

Arthur Pajot

65 ×1.5

SNP

100 ×2.3

AS

57 ×1.3

GPC

27 ×0.7

EE

42 ×1.1

AI

Citations per year, relative to Arthur Pajot Arthur Pajot (= 1×) peers Said Ouala

Countries citing papers authored by Arthur Pajot

Since Specialization

Citations

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

Fields of papers citing papers by Arthur Pajot

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arthur Pajot

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

All Works

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5 of 5 papers shown

1.

Bézenac, Emmanuel de, et al.. (2022). Modelling spatiotemporal dynamics from Earth observation data with neural differential equations. Machine Learning. 111(6). 2349–2380. 2 indexed citations

2.

Bézenac, Emmanuel de, et al.. (2020). Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations. 19. 3232–3236. 1 indexed citations

3.

Pajot, Arthur, Emmanuel de Bézenac, & Patrick Gallinari. (2018). Unsupervised Adversarial Image Reconstruction. International Conference on Learning Representations. 13 indexed citations

4.

Bézenac, Emmanuel de, et al.. (2018). Learning Partially Observed PDE Dynamics with Neural Networks. 1 indexed citations

5.

Bézenac, Emmanuel de, Arthur Pajot, & Patrick Gallinari. (2017). Deep Learning for Physical Processes: Incorporating Prior Scientific\n Knowledge. arXiv (Cornell University). 150 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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