...-driven application of machine learning (ML) models to rapidly predict the mechanical behavior and failure of cylindrical cells under different loading conditions. Mechanical abuse experiments including local indentation, flat compression, and three-point bending experiments were conducted on cylindrical...

... developing machine learning (ML) approach offers new opportunities and has attracted significant interest in the field. In this perspective paper, we highlight recent advancements in utilizing ML for designing printed structures with desired mechanical responses. First, we provide an overview of common...

...-cuttability) across different contexts, and extracting parallel information from papers to study in a systematic way. In applying machine learning to mechanics, language models can supplement or replace the need for traditional feature engineering by projecting text sequences to fixed-width vector...

... a machine learning regression model based on the open-source algorithm Extreme Gradient Boosting (XGBoost). The trained XGBoost machine learning model can then predict buckling loads of near-spherical shells with a small margin of error without time-consuming finite element simulations. Examples of near...

... the objective/constraint functions and external stimuli/boundary conditions are specified), an ultimate dream pursued by engineers in various disciplines, using machine learning (ML) techniques. To this end, the so-called moving morphable component (MMC)-based explicit framework for topology optimization...