Chen, Yanlai, and Koohy, Shawn. GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs. Retrieved from https://par.nsf.gov/biblio/10528800. Finite Elements in Analysis and Design 228.C Web. doi:10.1016/j.finel.2023.104047.

Chen, Yanlai, & Koohy, Shawn. GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs. Finite Elements in Analysis and Design, 228 (C). Retrieved from https://par.nsf.gov/biblio/10528800. https://doi.org/10.1016/j.finel.2023.104047

Chen, Yanlai, and Koohy, Shawn. "GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs". Finite Elements in Analysis and Design 228 (C). Country unknown/Code not available: Elsevier. https://doi.org/10.1016/j.finel.2023.104047. https://par.nsf.gov/biblio/10528800.

@article{osti_10528800,
place = {Country unknown/Code not available}, title = {GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs}, url = {https://par.nsf.gov/biblio/10528800}, DOI = {10.1016/j.finel.2023.104047}, abstractNote = {}, journal = {Finite Elements in Analysis and Design}, volume = {228}, number = {C}, publisher = {Elsevier}, author = {Chen, Yanlai and Koohy, Shawn}, }