Gao, Han, Zahr, Matthew J., and Wang, Jian-Xun. Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems. Retrieved from https://par.nsf.gov/biblio/10338460. Computer Methods in Applied Mechanics and Engineering 390.C Web. doi:10.1016/j.cma.2021.114502.

Gao, Han, Zahr, Matthew J., & Wang, Jian-Xun. Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems. Computer Methods in Applied Mechanics and Engineering, 390 (C). Retrieved from https://par.nsf.gov/biblio/10338460. https://doi.org/10.1016/j.cma.2021.114502

Gao, Han, Zahr, Matthew J., and Wang, Jian-Xun. "Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems". Computer Methods in Applied Mechanics and Engineering 390 (C). Country unknown/Code not available. https://doi.org/10.1016/j.cma.2021.114502. https://par.nsf.gov/biblio/10338460.

@article{osti_10338460,
place = {Country unknown/Code not available}, title = {Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems}, url = {https://par.nsf.gov/biblio/10338460}, DOI = {10.1016/j.cma.2021.114502}, abstractNote = {}, journal = {Computer Methods in Applied Mechanics and Engineering}, volume = {390}, number = {C}, author = {Gao, Han and Zahr, Matthew J. and Wang, Jian-Xun}, }