Direction-aware topological descriptors for Young's modulus prediction in porous materials
This paper introduces a direction-aware topological data analysis framework that embeds the compression axis into filtration functions to predict Young's modulus in porous materials, demonstrating superior accuracy over traditional direction-agnostic descriptors—particularly for anisotropic structures—while achieving performance comparable to convolutional neural networks with a more compact and transferable representation.