SK-PINN

SK-PINN: Accelerated physics-informed deep learning by smoothing kernel gradients

The automatic differentiation (AD) in the vanilla physics-informed neural networks (PINNs) is the computational bottleneck for the high-efficiency analysis. The concept of derivative discretization in smoothed particle hydrodynamics (SPH) can provide an accelerated training method for PINNs. In this paper, smoothing kernel physics-informed neural networks (SK-PINNs) are established, which solve differential equations using smoothing kernel discretization. It is a robust framework capable of solving problems in the computational mechanics of complex domains. When the number of collocation points gradually increases, the training speed of SK-PINNs significantly surpasses that of vanilla PINNs. In cases involving large collocation point sets or higher-order problems, SK-PINN training can be up to tens of times faster than vanilla PINN. Additionally, analysis using neural tangent kernel (NTK) theory shows that the convergence rates of SK-PINNs are consistent with those of vanilla PINNs. The superior performance of SK-PINNs is demonstrated through various examples, including regular and complex domains, as well as forward and inverse problems in fluid dynamics and solid mechanics.

Cite us

@article{Pan2025skpinn,
  author = {Pan, Cunliang and Li, Chengxuan and Liu, Yu and Zheng, Yonggang and Ye, Hongfei},
  title = {SK-PINN: Accelerated physics-informed deep learning by smoothing kernel gradients},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  volume = {440},
  pages={117956},
  year = {2025},
  doi = {10.1016/j.cma.2025.117956},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S0045782525002282}
>}

Computer Methods in Applied Mechanics and Engineering link：

https://www.sciencedirect.com/science/article/pii/S0045782525002282

arXiv link：

https://arxiv.org/abs/2411.02411