Author:
Impagnatiello Matteo,Bolla Michele,Keskinen Karri,Giannakopoulos George,Frouzakis Christos E.,Wright Yuri M.,Boulouchos Konstantinos
Subject
Fluid Flow and Transfer Processes,Mechanical Engineering,Condensed Matter Physics
Reference40 articles.
1. Deep learning in fluid dynamics;Kutz;J. Fluid Mech.,2017
2. Machine learning for fluid mechanics;Brunton;Annu. Rev. Fluid Mech.,2020
3. Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation;Wang;Phys. Fluids,2018
4. Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates;Lapeyre;Combust. Flame,2019
5. M. Bode, M. Gauding, Z. Lian, D. Denker, M. Davidovic, K. Kleinheinz, J. Jitsev, H. Pitsch, Using physics-informed super-resolution generative adversarial networks for subgrid modeling in turbulent reactive flows, arXiv preprint arXiv:1911.11380 (2019).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献