10 citations · 18 across the 4 of their papers we have counts for
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physics.flu-dyn2023★ 10 cited
Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks
Zhicheng Wang, Xuhui Meng, Xiaomo Jiang +2
Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for fluid flows and are especially effective for inverse and hybrid problems. However, vanilla PI…
eess.SY2023★ 7 cited
How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
Haodong Feng, Yue Wang, Hui Xiang +2
Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in the uniform flow arranged in an equilaterally triangular configuration, can learn th…