5 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
CauchyNet: Compact and Data-Efficient Learning using Holomorphic Activation Functions
Hong-Kun Zhang, Xin Li, Sikun Yang +1
A novel neural network inspired by Cauchy's integral formula, is proposed for function approximation tasks that include time series forecasting, missing data imputation, etc. Hence…
Enhancing Neural Function Approximation: The XNet Outperforming KAN
Xin Li, Xiaotao Zheng, Zhihong Xia
XNet is a single-layer neural network architecture that leverages Cauchy integral-based activation functions for high-order function approximation. Through theoretical analysis, we…
XNet-Enhanced Deep BSDE Method and Numerical Analysis
Xiaotao Zheng, Xingye Yue, Zhihong Xia +1
Semilinear parabolic partial differential equations (PDEs) are fundamental to modeling complex dynamical systems across scientific domains. The Deep Backward Stochastic Differentia…
Complex Physics-Informed Neural Network
Chenhao Si, Ming Yan, Xin Li +1
We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optim…