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Physics-informed ConvNet: Learning Physical Field from a Shallow Neural
  Network
v1v2 (latest)

Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network

Communications in nonlinear science & numerical simulation (CNSNS), 2022
26 January 2022
Peng Shi
Zhi Zeng
Tianshou Liang
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network"

2 / 2 papers shown
Title
Reconstruction of three-dimensional shapes of normal and disease-related erythrocytes from partial observations using multi-fidelity neural networks
Reconstruction of three-dimensional shapes of normal and disease-related erythrocytes from partial observations using multi-fidelity neural networks
Haizhou Wen
He Li
Zhen Li
52
0
0
18 Nov 2025
MLPs and KANs for data-driven learning in physical problems: A performance comparison
MLPs and KANs for data-driven learning in physical problems: A performance comparison
Raghav Pant
Sikan Li
Xingjian Li
Hassan Iqbal
Krishna Kumar
AI4CE
249
1
0
15 Apr 2025
1