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PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain

C++ 99.78% Python 0.22%

phygeonet's Introduction

PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Steady-State Parametric PDEs on Irregular Domain

Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Steady-State Parametric PDEs on Irregular Domain

Han Gao, Luning Sun, Jian-Xun Wang

Geometry-Adaptive

Citation

If you find this repo useful for your research, please consider to cite:

@article{gao_phygeonet:_2020,
	title = {{PhyGeoNet}: {Physics}-informed geometry-adaptive convolutional neural networks for solving {Parameterized} {Steady}-{State} {PDEs} on irregular domain},
	issn = {00219991},
	shorttitle = {{PhyGeoNet}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0021999120308536},
	doi = {10.1016/j.jcp.2020.110079},
	language = {en},
	urldate = {2021-01-04},
	journal = {Journal of Computational Physics},
	author = {Gao, Han and Sun, Luning and Wang, Jian-Xun},
	month = dec,
	year = {2020},
	pages = {110079},
}

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