501 citations · 546 across the 4 of their papers we have counts for
4 papers
A physics-driven sensor placement optimization methodology for temperature field reconstruction
Xu Liu, Wen Yao, Wei Peng +3
Perceiving the global field from sparse sensors has been a grand challenge in the monitoring, analysis, and design of physical systems. In this context, sensor placement optimizati…
RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
Zixue Xiang, Wei Peng, Wen Yao
Physics-informed neural networks (PINNs) have lately received significant attention as a representative deep learning-based technique for solving partial differential equations (PD…
Hybrid Finite Difference with the Physics-informed Neural Network for solving PDE in complex geometries
Zixue Xiang, Wei Peng, Weien Zhou +1
The physics-informed neural network (PINN) is effective in solving the partial differential equation (PDE) by capturing the physics constraints as a part of the training loss funct…
Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations
Zixue Xiang, Wei Peng, Xiaohu Zheng +2
There have been several efforts to Physics-informed neural networks (PINNs) in the solution of the incompressible Navier-Stokes fluid. The loss function in PINNs is a weighted sum…