5 papers
Subspace method based on neural networks for eigenvalue problems
Xiaoying Dai, Yunying Fan, Zhiqiang Sheng
In this paper, we propose a subspace method based on neural networks for eigenvalue problems with high accuracy and low cost. We first construct a neural network-based orthogonal b…
A model order reduction based adaptive parareal method for time-dependent partial differential equations
Xiaoying Dai, Miao Hu, Shuwei Shen
In this paper, we propose a model order reduction based adaptive parareal method for time-dependent partial differential equations. By using the data obtained by the fine propagato…
An orthogonality-preserving approach for eigenvalue problems
Tianyang Chu, Xiaoying Dai, Shengyue Wang +1
Solving large-scale eigenvalue problems poses a significant challenge due to the computational complexity and limitations on the parallel scalability of the orthogonalization opera…
A gradient flow model for the Gross--Pitaevskii problem: Mathematical and numerical analysis
Tianyang Chu, Xiaoying Dai, Jing Wu +1
This paper concerns the mathematical and numerical analysis of the normalized gradient flow model for the Gross--Pitaevskii eigenvalue problem, which has been widely used to…
Convergence of the adaptive finite element discretization based parallel orbital-updating method for eigenvalue problems
Xiaoying Dai, Yan Li, Bin Yang +1
It is significant and challenging to solve eigenvalue problems of partial differential operators when many highly accurate eigenpair approximations are required. The adaptive finit…