155 citations · 987 across the 104 of their papers we have counts for
107 papers
Uniform Inf-Sup Norm Equivalence and Robust Operator Preconditioning for Stokes Flow in tight domains with Periodic Pillars
Qi Xin, Yan Xie, Chen-song Zhang +2
Many microfluidic and porous-media computations reduce to the same core task: solving a Stokes saddle-point system on a domain perforated by a dense periodic array of pillars, as i…
Sharp Sobolev Approximation on General Domains by Linearized Shallow Networks with Analytic Activations
Jia Li, Tong Mao, Jinchao Xu
We study Sobolev approximation on bounded domains by linearized shallow neural networks whose inner parameters are prescribed independently of the target function. Our main step is…
Sharp Approximation on the Sphere by Linearized Shallow Networks with Analytic Activations
Jia Li, Tong Mao, Jinchao Xu
We study the spectral behavior and approximation properties of linearized shallow networks with analytic activation functions on the unit sphere . Motivated by earlier…
Optimal Neural Network Approximation via Empirical Least Squares with Deterministic Samples
Xinliang Liu, Tong Mao, Jinchao Xu
We develop a rigorous theory of discrete residual least-squares approximation for elliptic spectral equations using linearized ReLU neural networks on the sp…
Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View
Tong Mao, Jinchao Xu
Traditional approximation theory measures convergence rates in terms of the number of parameters or degrees of freedom. However, practical computation operates under finite precisi…
ReLU Neural de Rham Complexes
Kaibo Hu, Jindong Wang, Jinchao Xu
We construct finite-dimensional de Rham subcomplexes generated by fixed-neuron shallow ReLU neural networks, a class of spaces known to provide optimal approximation rates. For…