4 papers
Gradient-Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks
Jing Xiao, Xinhai Chen, Qinglin Wang +5
Training Physics-Informed Neural Networks (PINNs) requires jointly optimizing physics residual and initial/boundary condition loss terms, which often induce conflicting gradients.…
Prior-Guided Symbolic Regression: Towards Scientific Consistency in Equation Discovery
Jing Xiao, Xinhai Chen, Jiaming Peng +7
Symbolic Regression (SR) aims to discover interpretable equations from observational data, with the potential to reveal underlying principles behind natural phenomena. However, exi…
3DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generation
Jiaming Peng, Xinhai Chen, Jie Liu
Mesh generation is a crucial step in numerical simulations, significantly impacting simulation accuracy and efficiency. However, generating meshes remains time-consuming and requir…
Proposing an intelligent mesh smoothing method with graph neural networks
Zhichao Wang, Xinhai Chen, Junjun Yan +1
In CFD, mesh smoothing methods are commonly utilized to refine the mesh quality to achieve high-precision numerical simulations. Specifically, optimization-based smoothing is used…