31 citations · 60 across the 11 of their papers we have counts for
26 papers
Solving a class of multi-scale elliptic PDEs by means of Fourier-based mixed physics informed neural networks
Xi'an Li, Jinran Wu, You-Gan Wang +2
Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe int…
Understanding the Initial Condensation of Convolutional Neural Networks
Zhangchen Zhou, Hanxu Zhou, Yuqing Li +1
Previous research has shown that fully-connected networks with small initialization and gradient-based training methods exhibit a phenomenon known as condensation during training.…
Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion
Xiong-Bin Yan, Zhi-Qin John Xu, Zheng Ma
The use of Physics-informed neural networks (PINNs) has shown promise in solving forward and inverse problems of fractional diffusion equations. However, due to the fact that autom…
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width
Hanxu Zhou, Qixuan Zhou, Zhenyuan Jin +3
Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs…
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics
Tianhan Zhang, Yuxiao Yi, Yifan Xu +4
Machine learning has long been considered as a black box for predicting combustion chemical kinetics due to the extremely large number of parameters and the lack of evaluation stan…
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics
Zhiwei Wang, Yaoyu Zhang, Enhan Zhao +4
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics is proposed and validated using high-temperature auto-ignitions, perfectly stirred reactors…