A Method to Extract Physical Behavior Directly from Simple Visual Empirical Observation Via a Deep Learning Model, for a Material Agnostic Approach to Predicting Compressive Beam Buckling

Buckling is a long studied mechanical process that has been tackled from a variety of theoretical and numerical methods over the past two and a half centuries. Modeling buckling behavior of complicated structures—especially new composite material(s) in an expeditious manner remains an open question, which becomes more important as architected and smart materials come into modern consideration. Despite much research, predicting buckling behavior of materials with complex structure and components, such as notched beams of non-homogeneous architected composites, remains non-trivial. The present disclosure addresses the above problem by applying artificial intelligence methods to model physical relationships directly from observational data.