4 papers
Hypernetwork-Parameterized Spatially Adaptive Neural Operators for PDE Learning
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +7
Spatially heterogeneous partial differential equations (PDEs) exhibit location-dependent dynamics arising from variations in geometry and physical coefficients. Existing neural ope…
Multi-dimensional training-priority weighting based on physical information propagation paths: a unified residual-weighting framework for physics-informed neural networks
Zhangyi Lian, Xinda Dong, Wenxuan Huo +3
Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations (PDEs); however, their synchronous optimization treats residuals of different…
Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs
Wenxuan Huo, Qiang He, Gang Zhu +1
Partial differential equations (PDEs) serve as the cornerstone of mathematical physics. In recent years, Physics-Informed Neural Networks (PINNs) have significantly reduced the dep…
HL-nets: Physics-informed neural networks for hydrodynamic lubrication with cavitation
Yiqian Cheng, Qiang He, Weifeng Huang +3
Recently, physics-informed neural networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs). In this study, we establish a deep learning…