An airflow velocity field reconstruction method with sparse or incomplete data using physics-informed neural network

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Maziar Raissi, Paris Perdikaris, George Em Karniadakis · Journal of Computational Physics · 2018 · 16.9K citations · Full text

Natural Sciences, Computer Science, Physics +16

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Maziar Raissi, Alireza Yazdani, George Em Karniadakis · Science · 2020 · 2K citations · Full text

Computing, Computer Science, Physics +16

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

Luning Sun, Han Gao, Shaowu Pan et al. · Computer Methods in Applied Mechanics and Engineering · 2019 · 982 citations · Full text

Computing, Computational Engineering, Physics +12

Super-resolution reconstruction of turbulent flows with machine learning

Kai Fukami, Koji Fukagata, Kunihiko Taira · Journal of Fluid Mechanics · 2019 · 771 citations · Full text

Natural Sciences, Computer Science, Computational Fluid Dynamics +14

Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature

Zhao Zhang, Wei Zhang, Zhiqiang Zhai et al. · HVAC&R Research · 2007 · 733 citations

Engineering, Architectural Engineering, Computational Engineering +17