output
20182025
most citedSelf-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations

501 citations

14 papers

cs.SE2025★ 4 cited

Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?

Taiming Wang, Yuxia Zhang, Lin Jiang +3

Concise and meaningful method names are crucial for program comprehension and maintenance. However, method names may become inconsistent with their corresponding implementations, c…

q-bio.MN2023★ 2 cited

Universal scaling relation and criticality in metabolism and growth of Escherichia coli

Shaohua Guan, Zhichao Zhang, Zihan Zhang +1

The metabolic network plays a crucial role in regulating bacterial metabolism and growth, but it is subject to inherent molecular stochasticity. Previous studies have utilized flux…

physics.flu-dyn2023★ 59 cited

Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator

Yanfang Lyu, Xiaoyu Zhao, Zhiqiang Gong +2

Data-driven prediction of fluid flow and temperature distribution in marine and aerospace engineering has received extensive research and demonstrated its potential in real-time pr…

cs.LG2022★ 33 cited

RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network

Zixue Xiang, Wei Peng, Wen Yao

Physics-informed neural networks (PINNs) have lately received significant attention as a representative deep learning-based technique for solving partial differential equations (PD…

eess.SY2022★ 15 cited

Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network

Xu Liu, Wen Yao, Xiaohu Zheng +1

In the complex multi-state system (MSS), reliability analysis is a significant research content, both for equipment design, manufacturing, usage and maintenance. Universal Generati…

cs.CV2022★ 11 cited

A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification

Zhiqiang Gong, Ping Zhong, Jiahao Qi +1

Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic…

cs.LG2022★ 52 cited

Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data

Xu Liu, Wen Yao, Wei Peng +1

Physics-informed extreme learning machine (PIELM) has recently received significant attention as a rapid version of physics-informed neural network (PINN) for solving partial diffe…

cs.LG2022★ 96 cited

Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks

Xu Liu, Wei Peng, Zhiqiang Gong +2

Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have b…

cs.LG2021★ 33 cited

A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems

Xiaoqian Chen, Zhiqiang Gong, Xiaoyu Zhao +2

Temperature field reconstruction of heat source systems (TFR-HSS) with limited monitoring sensors occurred in thermal management plays an important role in real time health detecti…

physics.flu-dyn2021★ 501 cited

Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations

Zixue Xiang, Wei Peng, Xiaohu Zheng +2

There have been several efforts to Physics-informed neural networks (PINNs) in the solution of the incompressible Navier-Stokes fluid. The loss function in PINNs is a weighted sum…

math.NA2021★ 8 cited

Sparse Sampling Kaczmarz-Motzkin Method with Linear Convergence

Ziyang Yuan, Hui Zhang, Hongxia Wang

The randomized sparse Kaczmarz method was recently proposed to recover sparse solutions of linear systems. In this work, we introduce a greedy variant of the randomized sparse Kacz…

eess.IV2020★ 5 cited

Channel Attention Residual U-Net for Retinal Vessel Segmentation

Changlu Guo, Márton Szemenyei, Yangtao Hu +3

Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Resid…

cs.CL2018★ 416 cited

Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

Xiao Liu, Zhunchen Luo, Heyan Huang

Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extr…

q-bio.NC2018★ 2 cited

Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI

Yangsong Zhang, Erwei Yin, Fali Li +7

Canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) sys…