Correction to: Machine-Learning Based Design of Near-Spherical Shells under External Pressure (AIAA Scitech 2021 Forum)

Correction Notice Following figure label should be changed-Fig. 5 Moving one node and computing the buckling load (a) and (b) node trajectory (N_Train =180, N_Test = 20). Moving the two adjacent nodes and computing the buckling load (c) and (d) node trajectories (N_Train =450, N_Test = 50). Moving two opposite nodes and computing the buckling load (e) and (f) node trajectories (N_Train =405, N_Test = 45). Fig 6. Buckling load behavior against the node locations with R/T =100,200,300 Moving one node and computing the buckling load (a) and (b) node trajectory (N_Train =180, N_Test =20) Moving the two adjacent nodes and computing the buckling load (c) and (d) nodes trajectories (N_Train =270, N_Test =30). Fig 7. Buckling load behavior against different strip size. Correlation between the machine learning model and the FEA Method buckling load (a)(c) and (b) one strip width (N_Train =630, N_Test =70) and (d) five strip widths (N_Train =810, N_Test =90). Fig 8. Buckling load behavior against the ply angle and ply rotation: Changing the ply angle and ply rotation and computing buckling (a) (c) and ply stacking sequence (b)(d) respectively (N_Train =360, N_Test =40). Changing the ply angle and ply rotation of all five strips individually and computing the buckling load (e) and (f) grouping of the five strips (N_Train =594, N_Test =156).