Predicting compressive stress-strain behavior of elasto-plastic porous media via morphology-informed neural networks.

ABSTRACT: Porous media, ranging from bones to concrete and from batteries to architected lattices, pose difficult challenges in fully harnessing for engineering applications due to their complex and variable structures. Accurate and rapid assessment of their mechanical behavior is both challenging and essential, and traditional methods such as destructive testing and finite element analysis can be costly, computationally demanding, and time consuming. Machine learning (ML) offers a promising alternative for predicting mechanical behavior by leveraging data-driven correlations. However, with such structural complexity and diverse morphology among porous media, the question becomes how to effectively characterize these materials to provide robust feature spaces for ML that are descriptive, succinct, a

SUBMITTER: Lindqwister W 

PROVIDER: S-EPMC12008209 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature