5 papers
Neural Network Solvers for Nonlinear ODEs: Benchmarking MLPs and B-Spline/Chebyshev KANs
Mahidhar Reddy Patukuri, B. Veena S. N. Rao, S. M. Mallikarjunaiah
Three neural networks are compared as solvers of nonlinear ordinary differential equations: a multilayer perceptron (MLP), a Kolmogorov-Arnold network (KAN) whose edges are B-splin…
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…
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…
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…
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix
Kelum Gajamannage, Yonggi Park, S. M. Mallikarjunaiah +1
With the advent of sophisticated cameras, the urge to capture high-quality images has grown enormous. However, the noise contamination of the images results in substandard expectat…