JIA, Wang und XU, Hang, 2024. Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics. Physics of Fluids. 1 April 2024. Vol. 36, no. 4, p. 1-15. DOI 10.1063/5.0204237.
Elsevier - Harvard (with titles)Jia, W., Xu, H., 2024. Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics. Physics of Fluids 36, 1-15. https://doi.org/10.1063/5.0204237
American Psychological Association 7th editionJia, W., & Xu, H. (2024). Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics. Physics of Fluids, 36(4), 1-15. https://doi.org/10.1063/5.0204237
Springer - Basic (author-date)Jia W, Xu H (2024) Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics.. Physics of Fluids 36:1-15. https://doi.org/10.1063/5.0204237
Juristische Zitierweise (Stüber) (Deutsch)Jia, Wang/ Xu, Hang, Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics., Physics of Fluids 2024, 1-15.