10 citations · 27 across the 9 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
cs.LG2023★ 7 cited
Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions
Bin Lin, Zhiping Mao, Zhicheng Wang +1
Physics-informed Neural Networks (PINNs) have been shown as a promising approach for solving both forward and inverse problems of partial differential equations (PDEs). Meanwhile,…
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…