2 citations · 4 across the 14 of their papers we have counts for
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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…
MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation
Jing Xiao, Xinhai Chen, Qingling Wang +1
Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between ef…
GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing
Zhichao Wang, Xinhai Chen, Chunye Gong +5
Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of tradi…
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to cap…
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs),…