12 citations as recorded by crossref.

1. A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows P. Johnson 10.1017/jfm.2021.1150
2. Large eddy simulation of flow over a circular cylinder with a neural-network-based subgrid-scale model M. Kim et al. 10.1017/jfm.2024.154
3. A recursive neural-network-based subgrid-scale model for large eddy simulation: application to homogeneous isotropic turbulence C. Cho et al. 10.1017/jfm.2024.992
4. Frame invariant neural network closures for Kraichnan turbulence S. Pawar et al. 10.1016/j.physa.2022.128327
5. Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges M. Benjamin et al. 10.1140/epje/s10189-023-00314-6
6. Two neural network Unet architecture for subfilter stress modeling A. Wu & S. Lele 10.1103/PhysRevFluids.10.014601
7. Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation S. Agdestein & B. Sanderse 10.1016/j.jcp.2024.113577
8. Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model A. Ross et al. 10.1029/2022MS003258
9. Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES Y. Guan et al. 10.1016/j.physd.2022.133568
10. A posteriori study on wall modeling in large eddy simulation using a nonlocal data-driven approach G. Tabe Jamaat et al. 10.1063/5.0210851
11. Nitrate isotopes in catchment hydrology: Insights, ideas and implications for models I. Matiatos et al. 10.1016/j.jhydrol.2023.130326
12. Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning Y. Guan et al. 10.1016/j.jcp.2022.111090