110 citations · 150 across the 10 of their papers we have counts for
11 papers
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to cap…
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs),…
A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics
Zhuoying Zhao, Ziling Tan, Pinghui Mo +5
This paper proposes a special-purpose system to achieve high-accuracy and high-efficiency machine learning (ML) molecular dynamics (MD) calculations. The system consists of field p…
An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks
Xinhai Chen, Jie Liu, Junjun Yan +2
Mesh generation remains a key technology in many areas where numerical simulations are required. As numerical algorithms become more efficient and computers become more powerful, t…
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement
Hao Zhang, Tingting Wu, Siyao Cheng +1
As an emerging technology, federated learning (FL) involves training machine learning models over distributed edge devices, which attracts sustained attention and has been extensiv…
Inception Convolution with Efficient Dilation Search
Jie Liu, Chuming Li, Feng Liang +5
As a variant of standard convolution, a dilated convolution can control effective receptive fields and handle large scale variance of objects without introducing additional computa…