Craig Michoski
- Computational Mechanics top 5%
- Computational Fluid Dynamics and Aerodynamics 16
- Advanced Numerical Methods in Computational Mathematics 13
- Lattice Boltzmann Simulation Studies 4
- Fluid Dynamics and Turbulent Flows 3
- Numerical Analysis top 10%
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- Magnetic confinement fusion research 6
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- EEG and Brain-Computer Interfaces 5
- Neural dynamics and brain function 4
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- Emotion and Mood Recognition 4
- Co-authors
- Clint DawsonEthan J. KubatkoJoannes J. WesterinkD. R. HatchMiloš MilosavljevićTodd OliverDamrongsak WirasaetDongyang Kuang
- Journals
- Journal of Computational Physics (5 papers)Computer Methods in Applied Mechanics and Engineering (4 papers)Neurocomputing (2 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Craig Michoski
30 papers receiving 398 citations
Peers
Comparison fields: 5 of 65
- Computational Mechanics 211
- Numerical Analysis 45
- Statistical and Nonlinear Physics 71
- Nuclear and High Energy Physics 70
- Earth-Surface Processes 29
Countries citing papers authored by Craig Michoski
This map shows the geographic impact of Craig Michoski'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 Craig Michoski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Craig Michoski more than expected).
Fields of papers citing papers by Craig Michoski
This network shows the impact of papers produced by Craig Michoski. 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 Craig Michoski. The network helps show where Craig Michoski may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Craig Michoski, 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 | 2025 | 0 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 12 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 20 | |
| 7 | 2022 | 2 | |
| 8 | 2020 | 70 | |
| 9 | 2020 | 2 | |
| 10 | 2020 | 38 | |
| 11 | Quantifying and Propagating Uncertainties to Enhance Real-time Disruption Prediction with Machine Learning | 2018 | 1 |
| 12 | 2016 | 9 | |
| 13 | 2016 | 15 | |
| 14 | 2016 | 14 | |
| 15 | 2015 | 14 | |
| 16 | 2014 | 5 | |
| 17 | 2014 | 7 | |
| 18 | 2010 | 62 | |
| 19 | 2009 | 11 | |
| 20 | 2009 | 9 |
About Craig Michoski
Craig Michoski is a scholar working on Computational Mechanics, Numerical Analysis and Nuclear and High Energy Physics, having authored 31 papers that have together received 409 indexed citations. Recurring topics across this work include Computational Fluid Dynamics and Aerodynamics (16 papers), Advanced Numerical Methods in Computational Mathematics (13 papers), Magnetic confinement fusion research (6 papers), EEG and Brain-Computer Interfaces (5 papers), Neural dynamics and brain function (4 papers), Lattice Boltzmann Simulation Studies (4 papers), Emotion and Mood Recognition (4 papers) and Fluid Dynamics and Turbulent Flows (3 papers). The work is most often cited by research in Computational Mechanics (211 citations), Numerical Analysis (45 citations) and Statistical and Nonlinear Physics (71 citations). Craig Michoski has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Clint Dawson, Ethan J. Kubatko, Joannes J. Westerink, D. R. Hatch, Miloš Milosavljević, Todd Oliver, Damrongsak Wirasaet, Dongyang Kuang, John A. Evans and Corey J. Trahan. Their work appears in journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and Neurocomputing.
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.