3 papers
math.GM2026
CrackMorph-XAI-Net: A Topology-Preserving and Explainable Framework for Automated Crack Morphology
Sri Surya Pravallika Ajjarapu, S. M. Mallikarjunaiah
Automated crack inspection is increasingly recognized as a critical component of infrastructure monitoring; however, cracks continue to be reported primarily as binary segmentation…
math.GM2026
Learning constitutive laws under explicit strain limits: An interpretable strain-limiting elasticity--Kolmogorov Arnold neural network framework
Chandana Pati, S. M. Mallikarjunaiah
A physically consistent framework for modeling materials with saturating deformation, such as elastomers and biological tissues, is provided by strain-limiting elasticity. Fundamen…
cond-mat.mtrl-sci2025
Neural Networks as Physics-Consistent Surrogates: An \textit{Explainable AI} Validation Framework for Learning Constitutive Relations
Chandana Pati, S. M. Mallikarjunaiah
This paper presents a Physics-\textit{Explainable AI} (XAI) framework to validate and interpret neural networks for the constitutive modeling of solid materials. The study bridges…