47 citations · 83 across the 5 of their papers we have counts for
5 papers
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
Yuling Jiao, Di Li, Xiliang Lu +2
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differentia…
Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension
Yuling Jiao, Dingwei Li, Min Liu +2
In this paper, we consider recovering dimensional signals from binary measurements corrupted by noises and sign flips under the assumption that the target signals have low…
Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly
Yuling Jiao, Dingwei Li, Min Liu +1
Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning. Nonconvex penalized least squares have been attrac…
A rate of convergence of Physics Informed Neural Networks for the linear second order elliptic PDEs
Yuling Jiao, Yanming Lai, Dingwei Li +4
In recent years, physical informed neural networks (PINNs) have been shown to be a powerful tool for solving PDEs empirically. However, numerical analysis of PINNs is still missing…
More Industry-friendly: Federated Learning with High Efficient Design
Dingwei Li, Qinglong Chang, Lixue Pang +4
Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency. I…