54 citations · 163 across the 20 of their papers we have counts for
4 papers · 1 filter
A matrix preconditioning framework for physics-informed neural networks based on adjoint method
Jiahao Song, Wenbo Cao, Weiwei Zhang
Physics-informed neural networks (PINNs) have recently emerged as a popular approach for solving forward and inverse problems involving partial differential equations (PDEs). Compa…
Overcoming the Loss Conditioning Bottleneck in Optimization-Based PDE Solvers: A Novel Well-Conditioned Loss Function
Wenbo Cao, Weiwei Zhang
Optimization-based PDE solvers that minimize scalar loss functions have gained increasing attention in recent years. These methods either define the loss directly over discrete var…
FENN: Feature-enhanced neural network for solving partial differential equations involving fluid mechanics
Jiahao Song, Wenbo Cao, Weiwei Zhang
Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). However, PINNs sti…
Solving all laminar flows around airfoils all-at-once using a parametric neural network solver
Wenbo Cao, Shixiang Tang, Qianhong Ma +2
Recent years have witnessed increasing research interests of physics-informed neural networks (PINNs) in solving forward, inverse, and parametric problems governed by partial diffe…