Bernhard C. Geiger

1.3k citations
75 papers · 605 · h-index 14

Impact in

Papers in

Bernhard C. Geiger

70 papers receiving 583 citations

Peers

Bernhard C. Geiger
Comparison fields: 5 of 108
  • Fluid Flow and Transfer Processes 44
  • Artificial Intelligence 198
  • Computer Vision and Pattern Recognition 89
  • Aerospace Engineering 96
  • Statistical and Nonlinear Physics 47
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H.A. Spang United States
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Countries citing papers authored by Bernhard C. Geiger

Since Specialization
Citations

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

Fields of papers citing papers by Bernhard C. Geiger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bernhard C. Geiger. 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 Bernhard C. Geiger. The network helps show where Bernhard C. Geiger may publish in the future.

Co-authors

The 25 scholars most cited alongside Bernhard C. Geiger, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bernhard C. Geiger Line = papers co-authored together Bernhard C. Geiger links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019105
2 202130
3 201224
4 202122
5 201122
6 202319
7 202218
8 201917
9 201717
10 202216
11 202316
12 201715
13 202214
14 201314
15 202213
16 202213
17 201013
18 202411
19 202211
20 202210

About Bernhard C. Geiger

Bernhard C. Geiger is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Computational Theory and Mathematics and Statistical and Nonlinear Physics, having authored 75 papers that have together received 605 indexed citations. Recurring topics across this work include Neural Networks and Applications (8 papers), GNSS positioning and interference (6 papers), Advanced Combustion Engine Technologies (6 papers), Adversarial Robustness in Machine Learning (5 papers), Spectroscopy and Chemometric Analyses (4 papers), Stochastic Gradient Optimization Techniques (4 papers), Gaussian Processes and Bayesian Inference (4 papers) and Advanced Chemical Sensor Technologies (4 papers). The work is most often cited by research in Fluid Flow and Transfer Processes (44 citations), Artificial Intelligence (198 citations), Computer Vision and Pattern Recognition (89 citations), Aerospace Engineering (96 citations) and Statistical and Nonlinear Physics (47 citations). Bernhard C. Geiger has collaborated with scholars based in Austria, Germany and United States. Frequent co-authors include Rana Ali Amjad, Roman Kern, Christian Vogel, Stefan Posch, Gernot Kubin, Marek Śmieja, Patrick Ofner, Gerhard Pirker, F. Nadeem and Gorazd Kandus. Their work appears in journals such as IEEE Transactions on Aerospace and Electronic Systems, SAE technical papers on CD-ROM/SAE technical paper series, Applied Sciences, Information Sciences and Energy and AI.

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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