A. Pau

1.5k total citations
40 papers, 454 citations indexed

About

A. Pau is a scholar working on Nuclear and High Energy Physics, Aerospace Engineering and Materials Chemistry. According to data from OpenAlex, A. Pau has authored 40 papers receiving a total of 454 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Nuclear and High Energy Physics, 17 papers in Aerospace Engineering and 9 papers in Materials Chemistry. Recurrent topics in A. Pau's work include Magnetic confinement fusion research (33 papers), Nuclear reactor physics and engineering (11 papers) and Fusion materials and technologies (9 papers). A. Pau is often cited by papers focused on Magnetic confinement fusion research (33 papers), Nuclear reactor physics and engineering (11 papers) and Fusion materials and technologies (9 papers). A. Pau collaborates with scholars based in Switzerland, Italy and United Kingdom. A. Pau's co-authors include B. Cannas, G. Sias, Alessandra Fanni, A. Murari, O. Sauter, Sara Carcangiu, F. Rimini, P.C. de Vries, R. Granetz and the TCV Team and has published in prestigious journals such as Physical Review Letters, Nature Communications and IEEE Transactions on Nuclear Science.

In The Last Decade

A. Pau

34 papers receiving 428 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A. Pau Switzerland 14 332 133 109 101 84 40 454
Kevin Montes United States 9 272 0.8× 119 0.9× 92 0.8× 91 0.9× 57 0.7× 11 378
Cristina Rea United States 15 473 1.4× 186 1.4× 147 1.3× 137 1.4× 112 1.3× 39 657
R. A. Tinguely United States 11 382 1.2× 159 1.2× 77 0.7× 167 1.7× 104 1.2× 41 531
Dalong Chen China 13 327 1.0× 127 1.0× 99 0.9× 107 1.1× 85 1.0× 62 493
D. Alves Portugal 10 307 0.9× 114 0.9× 70 0.6× 98 1.0× 40 0.5× 61 420
C. Tichmann Germany 12 302 0.9× 97 0.7× 53 0.5× 85 0.8× 131 1.6× 20 395
M. Johnson United Kingdom 10 571 1.7× 166 1.2× 103 0.9× 207 2.0× 202 2.4× 18 706
M. Baruzzo Italy 16 543 1.6× 173 1.3× 58 0.5× 202 2.0× 210 2.5× 49 626
P.C. de Vries Germany 15 676 2.0× 169 1.3× 99 0.9× 276 2.7× 250 3.0× 22 795
D. Hwang United States 13 264 0.8× 97 0.7× 75 0.7× 82 0.8× 108 1.3× 25 502

Countries citing papers authored by A. Pau

Since Specialization
Citations

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

Fields of papers citing papers by A. Pau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. Pau

This figure shows the co-authorship network connecting the top 25 collaborators of A. Pau. A scholar is included among the top collaborators of A. Pau 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 A. Pau. A. Pau 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.
Labit, B., B.P. Duval, A. Karpushov, et al.. (2025). L–H power threshold for neutral beam heated plasmas with deuterium, hydrogen, helium and mixed ion species in TCV. Plasma Physics and Controlled Fusion. 67(5). 55010–55010.
2.
Piron, L., S. Aleiferis, O. Sauter, et al.. (2025). P sep/P LH control in deuterium and deuterium–tritium JET plasmas. Plasma Physics and Controlled Fusion. 67(5). 55006–55006.
3.
Sieglin, B., A. Gude, F. Felici, et al.. (2025). H-Mode density limit disruption avoidance in ASDEX Upgrade, TCV and JET. Fusion Engineering and Design. 215. 114961–114961. 1 indexed citations
4.
Pau, A., Cristina Rea, O. Sauter, et al.. (2025). Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV. Nature Communications. 16(1). 8877–8877.
5.
Piron, L., et al.. (2025). Machine learning methods for locked-mode predictions in MAST-U plasmas. Plasma Physics and Controlled Fusion. 67(4). 45007–45007.
6.
Pau, A., et al.. (2025). Plasma state monitoring and disruption characterization using multimodal VAEs. Nuclear Fusion. 65(9). 96012–96012. 1 indexed citations
7.
Rossi, Riccardo, A. Murari, T. Craciunescu, et al.. (2025). Time-resolved, physics-informed neural networks for tokamak total emission reconstruction and modelling. Nuclear Fusion. 65(3). 36030–36030. 1 indexed citations
8.
Piron, L., S. Aleiferis, L. Garzotti, et al.. (2024). Innovative dud detection based on JET DT experience. Fusion Engineering and Design. 200. 114155–114155.
9.
Labit, B., O. Sauter, T. Pütterich, et al.. (2024). Progress in the development of the ITER baseline scenario in TCV. Plasma Physics and Controlled Fusion. 66(2). 25016–25016. 5 indexed citations
10.
Pau, A., et al.. (2023). A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks. Nuclear Fusion. 63(5). 56019–56019. 18 indexed citations
11.
Sieglin, B., M. Maraschek, A. Gude, et al.. (2023). Disruption avoidance and investigation of the H-Mode density limit in ASDEX Upgrade. Plasma Physics and Controlled Fusion. 66(2). 25004–25004. 8 indexed citations
12.
Giacomin, M., A. Pau, Paolo Ricci, et al.. (2022). First-Principles Density Limit Scaling in Tokamaks Based on Edge Turbulent Transport and Implications for ITER. Physical Review Letters. 128(18). 185003–185003. 40 indexed citations
13.
Menkovski, Vlado, et al.. (2021). Plasma confinement mode classification using a sequence-to-sequence neural network with attention. Nuclear Fusion. 61(4). 46019–46019. 8 indexed citations
14.
Felici, F., C. Galperti, M. Maraschek, et al.. (2021). Integrated Real-Time Supervisory Management for Off-Normal-Event Handling and Feedback Control of Tokamak Plasmas. IEEE Transactions on Nuclear Science. 68(8). 1855–1861. 11 indexed citations
15.
Pau, A., M. Maraschek, F. Felici, et al.. (2020). Active disruption avoidance for H-mode density limits on TCV and ASDEX Upgrade. MPG.PuRe (Max Planck Society). 1 indexed citations
16.
Aymerich, E., Alessandra Fanni, G. Sias, et al.. (2020). A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET. Nuclear Fusion. 61(3). 36013–36013. 22 indexed citations
17.
Rea, Cristina, Kevin Montes, A. Pau, R. Granetz, & O. Sauter. (2020). Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks. Fusion Science & Technology. 76(8). 912–924. 27 indexed citations
18.
Pau, A., Alessandra Fanni, B. Cannas, et al.. (2018). A First Analysis of JET Plasma Profile-Based Indicators for Disruption Prediction and Avoidance. IEEE Transactions on Plasma Science. 46(7). 2691–2698. 33 indexed citations
19.
Jachmich, S., P. Drewelow, S. Gerasimov, et al.. (2016). Disruption mitigation at JET using massive gas injection. Max Planck Digital Library. 3 indexed citations
20.
Pau, A.. (2014). Techniques for prediction of disruptions on TOKAMAKS. Padua@research (University of Padova). 2 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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