| Machine learning informed predictor importance measures of environmental parameters in maritime optical turbulence C Jellen, J Burkhardt, C Brownell, C Nelson Applied Optics 59 (21), 6379-6389, 2020 | 28 | 2020 |
| Machine-learning informed macro-meteorological models for the near-maritime environment C Jellen, M Oakley, C Nelson, J Burkhardt, C Brownell Applied Optics 60 (11), 2938-2951, 2021 | 21 | 2021 |
| Measurement and analysis of atmospheric optical turbulence in a near-maritime environment C Jellen, C Nelson, C Brownell, J Burkhardt, M Oakley IOP SciNotes 1 (2), 024006, 2020 | 20 | 2020 |
| Hybrid optical turbulence models using machine-learning and local measurements C Jellen, C Nelson, J Burkhardt, C Brownell Applied Optics 62 (18), 4880-4890, 2023 | 3 | 2023 |
| Long-term measurement and characterization of boundary layer optical turbulence C Jellen, C Nelson, C Brownell, J Burkhardt JOSA A 41 (6), B65-B72, 2024 | 1 | 2024 |
| Effective benchmarks for optical turbulence modeling C Jellen, C Nelson, C Brownell, J Burkhardt arXiv preprint arXiv:2401.03573, 2024 | 1 | 2024 |
| Selection of features for an image-based machine learning model to predict atmospheric optical turbulence S Schork, C Jellen, C Nelson, J Burkhardt, C Brownell APS Division of Fluid Dynamics Meeting Abstracts, N01. 044, 2021 | | 2021 |
| Using machine learning to predict low-altitude atmospheric optical turbulence C Jellen, J Burkhardt, C Nelson, C Brownell APS Division of Fluid Dynamics Meeting Abstracts, Q17. 007, 2019 | | 2019 |
Cody BrownellUnited States Naval Academy在 usna.edu 的电子邮件经过验证
Cody BrownellUnited States Naval Academy在 usna.edu 的电子邮件经过验证
John BurkhardtProfessor of Mechanical Engineering, United States Naval Academy在 usna.edu 的电子邮件经过验证