Research output per year
Research output per year
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Dive into the research topics where Moritz Linkmann is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Research output
- 29 Article
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Interpreted machine learning in fluid dynamics: explaining relaminarisation events in wall-bounded shear flows
Lellep, M., Prexl, J., Eckhardt, B. & Linkmann, M., 13 May 2022, In: Journal of Fluid Mechanics. 942, A2.Research output: Contribution to journal › Article › peer-review
Open Access -
Data-driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition
Yang, R., Zhang, X., Reiter, P., Lohse, D., Shishkina, O. & Linkmann, M., 10 Mar 2022, In: GAMM Mitteilungen: Surveys for Applied Mathematics and Mechanics (GAMM). 45, 1, e202200003.Research output: Contribution to journal › Article › peer-review
Open Access -
Purely elastic linear instabilities in parallel shear flows with free-slip boundary conditions
Lellep, M., Linkmann, M., Eckhardt, B. & Morozov, A., 10 Dec 2021, In: Journal of Fluid Mechanics. 928, R3, p. 1-11 11 p., 928 .Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Effects of Forcing Mechanisms on the Multiscale Properties of Magnetohydrodynamics
Yang, Y., Linkmann, M., Biferale, L. & Wan, M., 16 Mar 2021, In: Astrophysical Journal. 909, 13 p., 175.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Linear feedback control of invariant solutions in channel flow
Linkmann, M., Knierim, F., Zammert, S. & Eckhardt, B., 10 Oct 2020, In: Journal of Fluid Mechanics. 900, A10.Research output: Contribution to journal › Article › peer-review
Projects
- 2 Active
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SPF ExCALIBUR EX20-10 Machine Learning
UK central government bodies/local authorities, health and hospital authorities
1/06/21 → 31/05/24
Project: Research
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Superstructures and exact coherent states in the asymptotic suction boundary layer
1/05/20 → 30/09/24
Project: Research
Datasets
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Data for "Interpreted machine learning in fluid dynamics" Lellep et al., Journal of Fluid Mechanics, 2022
Linkmann, M. (Creator) & Lellep, M. (Creator), Edinburgh DataShare, 4 May 2022
DOI: 10.7488/ds/3450, https://arxiv.org/abs/2102.05541
Dataset