DeepBuckle: Extracting physical behavior directly from empirical observation for a material agnostic approach to analyze and predict 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. Despite this, predicting buckling behavior of materials with complex structure and components, such as notched beams of non-homogeneous architected composites, remains non-trivial. Inspired by recent advancements in applying artificial intelligence methods to model physical relationships directly from observational data, here we use a Variational Autoencoder in concert with a Long Short-Term Memory network to model the buckling behavior of notched beams. Our model, DeepBuckle, qualitatively and quantitatively learns buckling behavior of homogeneous polymer beams, and has the capacity to predict novel designs that yield certain buckling behaviors with creative, out-of-the-box implementation. Importantly, we subsequently demonstrate that our approach directly generalizes to beams comprised of a far more complex composite foam material, without the increased computational resources or ancillary knowledge of material characteristics required by a more traditional finite element or other continuum approaches. Notably, the method reported here uses a simple table top experimental setup and can easily be transferred to other applications, for use in fundamental studies of mechanical phenomena, or in educational settings. [ABSTRACT FROM AUTHOR]