Sam Dillavou
Impact in
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- Neural Networks and Reservoir Computing
- Neural Networks and Applications
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- Adhesion, Friction, and Surface Interactions
Papers in
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- Neural Networks and Reservoir Computing 5
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- Adhesion, Friction, and Surface Interactions 4
- Co-authors
- Shmuel M. Rubinstein (4 shared papers)D. J. Durian (10 shared papers)Andrea J. Liu (6 shared papers)Menachem Stern (5 shared papers)Jesse L. Silverberg (1 shared paper)Lawrence J. Bonassar (1 shared paper)Itai Cohen (1 shared paper)Marc Z. Miskin (1 shared paper)
- Journals
- Soft Matter (2 papers)Physical Review Letters (2 papers)Physical review. E (2 papers)Physical Review Applied (1 paper)Scientific Reports (1 paper)
- Partner nations
- United StatesIsraelSwitzerland
In The Last Decade
Sam Dillavou
17 papers receiving 229 citations
Peers
Comparison fields: 5 of 69
- Artificial Intelligence 63
- Mechanics of Materials 48
- Rheumatology 25
- Geophysics 19
- Automotive Engineering 16
Countries citing papers authored by Sam Dillavou
This map shows the geographic impact of Sam Dillavou'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 Sam Dillavou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Dillavou more than expected).
Fields of papers citing papers by Sam Dillavou
This network shows the impact of papers produced by Sam Dillavou. 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 Sam Dillavou. The network helps show where Sam Dillavou may publish in the future.
Co-authors
The 25 scholars most cited alongside Sam Dillavou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 53 | |
| 2 | 2022 | 50 | |
| 3 | 2012 | 32 | |
| 4 | 2022 | 21 | |
| 5 | 2024 | 18 | |
| 6 | 2020 | 16 | |
| 7 | 2023 | 13 | |
| 8 | 2023 | 9 | |
| 9 | 2022 | 8 | |
| 10 | 2024 | 5 | |
| 11 | 2022 | 4 | |
| 12 | 2024 | 2 | |
| 13 | 2021 | 2 | |
| 14 | 2025 | 2 | |
| 15 | 2025 | 1 | |
| 16 | 2023 | 1 | |
| 17 | 2024 | 1 |
About Sam Dillavou
Sam Dillavou is a scholar working on Artificial Intelligence, Mechanics of Materials, Electrical and Electronic Engineering, Geophysics and Biomedical Engineering, having authored 17 papers that have together received 238 indexed citations. Recurring topics across this work include Neural Networks and Reservoir Computing (5 papers), Advanced Memory and Neural Computing (5 papers), Adhesion, Friction, and Surface Interactions (4 papers), High-pressure geophysics and materials (4 papers), Fluid Dynamics and Heat Transfer (2 papers), earthquake and tectonic studies (2 papers), Sports Dynamics and Biomechanics (2 papers) and Ferroelectric and Negative Capacitance Devices (2 papers). The work is most often cited by research in Artificial Intelligence (63 citations), Mechanics of Materials (48 citations), Rheumatology (25 citations), Geophysics (19 citations) and Automotive Engineering (16 citations). Sam Dillavou has collaborated with scholars based in United States, Israel and Switzerland. Frequent co-authors include Shmuel M. Rubinstein, D. J. Durian, Andrea J. Liu, Menachem Stern, Jesse L. Silverberg, Lawrence J. Bonassar, Itai Cohen, Marc Z. Miskin, William Steinhardt and E. E. Brodsky. Their work appears in journals such as Soft Matter, Physical Review Letters, Physical review. E, Physical Review Applied and Scientific Reports.
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.