2 citations · 2 across the 6 of their papers we have counts for
8 papers
Factored Sparse Approximate Inverse Preconditioning via Spectral Optimization
Francesco Brarda, Tianshi Xu, Vassilis Kalantzis +2
In this paper, we study value selection for fixed-pattern factorized sparse approximate inverse preconditioners. Given a prescribed sparsity pattern for a factor we choose its…
Neural Approximate Inverse Preconditioners
Tianshi Xu, Rui Peng Li, Yuanzhe Xi
In this paper, we propose a data-driven framework for constructing efficient approximate inverse preconditioners for elliptic partial differential equations (PDEs) by learning the…
Preconditioned Additive Gaussian Processes with Fourier Acceleration
Theresa Wagner, Tianshi Xu, Franziska Nestler +2
Gaussian processes (GPs) are crucial in machine learning for quantifying uncertainty in predictions. However, their associated covariance matrices, defined by kernel functions, are…
HiGP: A high-performance Python package for Gaussian Process
Hua Huang, Tianshi Xu, Yuanzhe Xi +1
Gaussian Processes (GPs) are flexible, nonparametric Bayesian models widely used for regression and classification because of their ability to capture complex data patterns and qua…
Multiscale Neural Networks for Approximating Green's Functions
Wenrui Hao, Rui Peng Li, Yuanzhe Xi +2
Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solv…
An Adaptive Factorized Nyström Preconditioner for Regularized Kernel Matrices
Shifan Zhao, Tianshi Xu, Hua Huang +2
The spectrum of a kernel matrix significantly depends on the parameter values of the kernel function used to define the kernel matrix. This makes it challenging to design a precond…