1 citations · 1 across the 7 of their papers we have counts for
7 papers
Spectral Convergence of Random Feature Method in Multiple Dimensions
Pingbing Ming, Hao Yu
We first prove spectral convergence of the random feature method (RFM) for multidimensional targets in Sobolev, Gevrey, ultra-analytic, and bandlimited classes. The analysis establ…
Sharp Mixed Spectral Barron Regularity of Coulombic Many-Electron Wave Functions
Pingbing Ming, Hao Yu
We establish sharp mixed spectral Barron regularity for eigenfunctions of molecular Coulomb Hamiltonians. The mixed norm is a Fourier norm with one isotropic weight and coord…
Sharp Barron Regularity Results for Coulombic Many-Electron Wave Functions
Pingbing Ming, Hao Yu
We establish sharp Barron regularity for Coulombic many-electron wave functions after extraction of the universal cut-off Jastrow factors. Following the factorization of Fournais e…
Barron regularity of many particle Schrödinger eigenfunctions
Pingbing Ming, Hao Yu
This work investigates the regularity of Schrödinger eigenfunctions and the solvability of Schrödinger equations in spectral Barron space , where…
Sharp uniform approximation for spectral Barron functions by deep neural networks
Yulei Liao, Pingbing Ming, Hao Yu
This work explores the neural network approximation capabilities for functions within the spectral Barron space , where is the smoothness index. We demonstrate t…
Spectral convergence of random feature method in one dimension
Pingbing Ming, Hao Yu
We first prove the spectral convergence of the random feature method (RFM) when used to solve second-order elliptic equations and eigenvalue problems in one dimension, provided tha…
Generalization Error Estimates of Machine Learning Methods for Solving High Dimensional Schrödinger Eigenvalue Problems
Hao Yu, Yixiao Guo, Pingbing Ming
We propose a machine learning method for computing eigenvalues and eigenfunctions of the Schrödinger operator on a -dimensional hypercube with Dirichlet boundary conditions. The…