no code implementations • 14 Nov 2023 • Han Gao, Xu Han, Xiantao Fan, Luning Sun, Li-Ping Liu, Lian Duan, Jian-Xun Wang
A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes.
1 code implementation • 3 Oct 2019 • Heng Xiao, Jin-Long Wu, Sylvain Laizet, Lian Duan
However, a major obstacle in the development of data-driven turbulence models is the lack of training data.
Fluid Dynamics
no code implementations • 19 Aug 2018 • Jian-Xun Wang, Junji Huang, Lian Duan, Heng Xiao
This study demonstrates that the PIML approach is a computationally affordable technique for improving the accuracy of RANS-modeled Reynolds stresses for high-Mach-number turbulent flows when there is a lack of experiments and high-fidelity simulations.
BIG-bench Machine Learning Physics-informed machine learning
1 code implementation • 5 Mar 2018 • Lian Duan, Xi Qin, Yuanhao He, Xialin Sang, Jinda Pan, Tao Xu, Jing Men, Rudolph E. Tanzi, Airong Li, Yutao Ma, Chao Zhou
Convolutional neural networks are powerful tools for image segmentation and classification.