Gao, Han, Sun, Luning, and Wang, Jian-Xun. PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain. Retrieved from https://par.nsf.gov/biblio/10311904. Journal of Computational Physics 428.C Web. doi:10.1016/j.jcp.2020.110079.

Gao, Han, Sun, Luning, & Wang, Jian-Xun. PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain. Journal of Computational Physics, 428 (C). Retrieved from https://par.nsf.gov/biblio/10311904. https://doi.org/10.1016/j.jcp.2020.110079

Gao, Han, Sun, Luning, and Wang, Jian-Xun. "PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain". Journal of Computational Physics 428 (C). Country unknown/Code not available. https://doi.org/10.1016/j.jcp.2020.110079. https://par.nsf.gov/biblio/10311904.

@article{osti_10311904,
place = {Country unknown/Code not available}, title = {PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain}, url = {https://par.nsf.gov/biblio/10311904}, DOI = {10.1016/j.jcp.2020.110079}, abstractNote = {}, journal = {Journal of Computational Physics}, volume = {428}, number = {C}, author = {Gao, Han and Sun, Luning and Wang, Jian-Xun}, }