Exploring AI-driven computational dynamic modeling and nanomechanical reinforcement for enhanced nonlinear post-buckling in plates: A comprehensive computer analysis

AI driven computational dynamic modeling and nanomechanical reinforcement of functionally graded (FG) plates resting on polymeric foundation are considered in the study and improvements of nonlinear post buckling behavior are assessed. Nonlinear characteristics are significantly improved by a proper exploitation of the superior mechanical properties of carbon nanotubes (CNTs). This analysis is enabled by a modified mixture homogenization approach coupled with a high order shear deformation theory (HSDT), which is useful in analyzing the distribution of nanocomposite property across the plate thickness. In the present research, key parameters such as CNT volume fraction, foundation stiffness, nanoparticle agglomeration, and dynamic loading intensity are studied comprehensively under nonlinear interactions to evaluate the bearing ability of system stability and dynamic response. In order to capture the complete nonlinear postbuckling and dynamic behavior, an advanced computational framework unites AI driven modeling with both energy principles and domain decomposition, Rayleigh-Ritz methods, and Newton Raphson numerical iteration. The results indicate that CNT reinforcement improves load bearing capacity and structural stability and polymeric composition has a pronounced effect on nonlinear dynamic properties in the form of damping and stability performance. For the front engineering of FG nanocomposite structures that enhance their mechanical resilience, this research is yielding critical learning on computational strategies for encouraging AI assisted material design and structural optimization.