Data‐driven projection method in fluid simulation
By Cheng Yang, Xubo Yang, Xiangyun Xiao. 2016
138 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Nils Thürey
- Phaedon-Stelios Koutsourelakis
- Yang Liu
- Paris Perdikaris
- Chengping Rao
- Suvranu De
- Rahul
- Zhongqing Han
In Computer Graphics and Computer-Aided Design
- Suvranu De
- Rahul
- Zhongqing Han
- Nils Thürey
- Theodore Kim
- Nicholas Zabaras
- Barbara Solenthaler
- Markus H. Gross
In Computer Vision and Pattern Recognition
- Nils Thürey
- Suvranu De
- Rahul
- Zhongqing Han
- Yang Liu
- Theodore Kim
- Nicholas Zabaras
- Barbara Solenthaler
Citations in the corpus
- Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data (2019, background): “Applications in flow simulations include pressure projections in solving Navier-Stokes equations [8], fluid flow through random heterogeneous media [9, 10, 11],”
- A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery (2019, methodology): “CNNs have been shown to be effective in the simulation of the Navier-Stokes equations [22,27].”
- Deep Fluids: A Generative Network for Parameterized Fluid Simulations (2018, methodology): “For a single time step, a CNN-based pressure projection was likewise proposed [Tompson et al. 2016; Yang et al. 2016].”
- Physics-informed deep learning for incompressible laminar flows (2020, background): “, see [3, 4, 7]).”