most citedPhysics-Informed Kernel Function Neural Networks for Solving Partial Differential Equations

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.CR2025

Privacy Loss of Noise Perturbation via Concentration Analysis of A Product Measure

Shuainan Liu, Tianxi Ji, Zhongshuo Fang +2

Noise perturbation is one of the most fundamental approaches for achieving -differential privacy (DP) guarantees when releasing the result of a query or function $f(\cdot)\i…

math.NA2023★ 2 cited

Physics-Informed Kernel Function Neural Networks for Solving Partial Differential Equations

Zhuojia Fu, Wenzhi Xu, Shuainan Liu

This paper proposed a novel radial basis function neural network (RBFNN) to solve various partial differential equations (PDEs). In the proposed RBF neural networks, the physics-in…