110 citations · 150 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors
Jie Liu, Jiawen Liu, Zhen Xie +1
How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices. Auto-labeling data on mobile devi…
Model Embedded DRL for Intelligent Greenhouse Control
Tinghao Zhang, Jingxu Li, Jingfeng Li +3
Greenhouse environment is the key to influence crops production. However, it is difficult for classical control methods to give precise environment setpoints, such as temperature,…