研究论文
SK-PINN: Accelerated physics-informed deep learning by smoothing kernel gradients
Cunliang Pan, Chengxuan Li, Yü Liu, Yonggang Zheng, Hongfei Ye
Dalian University of Technology Joint Institute of the Dalian University of Technology and Belarusian State University
来源Computer Methods in Applied Mechanics and Engineering
年份2025
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逐年被引趋势
1370
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1326
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23
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12.62
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同类平均 = 1
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学术脉络
学科主题
物理Model Reduction and Neural Networks
Nuclear reactor physics and engineering · Seismic Imaging and Inversion Techniques
参考文献 81
The Combined Finite‐Discrete Element Method
被引 1,385Ante Munjiza · 2004
Spectral Methods: Algorithms, Analysis and Applications
被引 1,020Jie Shen, Tao Tang, Li-Lian Wang · DIAL (Catholic University of Leuven) · 2011
此处列出前 3 条
引用本文 23
Embedding Physics into Machine Learning: A Review of Physics Informed Neural Networks as Partial Differential Equation Forward Solvers
被引 10Wenhui Fan, Xujia Chen · Tsinghua Science & Technology · 2025
Legend-KINN: A legendre polynomial-based Kolmogorov–Arnold-informed neural network for efficient PDE solving
被引 9Zhuo Zhang, Xiong Xiong, Sen Zhang · Expert Systems with Applications · 2025
A novel physics-informed neural network via field mapping
被引 5Bo-Xun Sun, Chen-Xu Liu, Gui‐Lan Yu · International Journal of Mechanical Sciences · 2026
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