Park, Donggeun, Jung, Jiyoung, Gu, Grace X., and Ryu, Seunghwa. A generalizable and interpretable deep learning model to improve the prediction accuracy of strain fields in grid composites. Retrieved from https://par.nsf.gov/biblio/10451509. Materials & Design 223.C Web. doi:10.1016/j.matdes.2022.111192.

Park, Donggeun, Jung, Jiyoung, Gu, Grace X., & Ryu, Seunghwa. A generalizable and interpretable deep learning model to improve the prediction accuracy of strain fields in grid composites. Materials & Design, 223 (C). Retrieved from https://par.nsf.gov/biblio/10451509. https://doi.org/10.1016/j.matdes.2022.111192

Park, Donggeun, Jung, Jiyoung, Gu, Grace X., and Ryu, Seunghwa. "A generalizable and interpretable deep learning model to improve the prediction accuracy of strain fields in grid composites". Materials & Design 223 (C). Country unknown/Code not available. https://doi.org/10.1016/j.matdes.2022.111192. https://par.nsf.gov/biblio/10451509.

@article{osti_10451509,
place = {Country unknown/Code not available}, title = {A generalizable and interpretable deep learning model to improve the prediction accuracy of strain fields in grid composites}, url = {https://par.nsf.gov/biblio/10451509}, DOI = {10.1016/j.matdes.2022.111192}, abstractNote = {}, journal = {Materials & Design}, volume = {223}, number = {C}, author = {Park, Donggeun and Jung, Jiyoung and Gu, Grace X. and Ryu, Seunghwa}, }