no code implementations • 7 Apr 2024 • Zhiqiang Cai, Tong Ding, Min Liu, Xinyu Liu, Jianlin Xia
In this paper, we propose a structure-guided Gauss-Newton (SgGN) method for solving least squares problems using a shallow ReLU neural network.
no code implementations • 7 Mar 2024 • Jialin Chen, Zhiqiang Cai, Ke Xu, Di wu, Wei Cao
Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates.
no code implementations • 9 Sep 2022 • Jiayue Han, Zhiqiang Cai, Zhiyou Wu, Xiang Zhou
Thus, we propose the Residual-Quantile Adjustment (RQA) method for a better weight choice for each training sample.
no code implementations • 19 Nov 2021 • Zhiqiang Cai, Ling Lin, Xiang Zhou
We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution.
no code implementations • 21 Oct 2021 • Zhiqiang Cai, Jingshuang Chen, Min Liu
A least-squares neural network (LSNN) method was introduced for solving scalar linear and nonlinear hyperbolic conservation laws (HCLs) in [7, 6].
no code implementations • 29 Sep 2021 • Min Liu, Zhiqiang Cai, Karthik Ramani
This paper presents RitzNet, an unsupervised learning method which takes any point in the computation domain as input, and learns a neural network model to output its corresponding function value satisfying the underlying governing PDEs.
no code implementations • 7 Sep 2021 • Zhiqiang Cai, Jingshuang Chen, Min Liu
Designing an optimal deep neural network for a given task is important and challenging in many machine learning applications.
no code implementations • 25 May 2021 • Zhiqiang Cai, Jingshuang Chen, Min Liu
This paper studies least-squares ReLU neural network method for solving the linear advection-reaction problem with discontinuous solution.
no code implementations • 25 May 2021 • Zhiqiang Cai, Jingshuang Chen, Min Liu
We introduced the least-squares ReLU neural network (LSNN) method for solving the linear advection-reaction problem with discontinuous solution and showed that the method outperforms mesh-based numerical methods in terms of the number of degrees of freedom.
1 code implementation • 5 Nov 2019 • Zhiqiang Cai, Jingshuang Chen, Min Liu, Xinyu Liu
This paper studies an unsupervised deep learning-based numerical approach for solving partial differential equations (PDEs).
no code implementations • 12 Feb 2016 • Tai Wang, Xiangen Hu, Keith Shubeck, Zhiqiang Cai, Jie Tang
The relationship between reading and writing (RRW) is one of the major themes in learning science.