In this present paper, we introduce an advanced Physics-Informed Neural Network (PINN)
based Topology Optimization (TO) framework for addressing complex structural design …

Physics-informed deep learning has emerged as a promising approach that incorporates
physical constraints into the model, reduces the amount of data required, and demonstrates …

This review examines the current state of research on microscale cellular changes during
the drying of plant-based food materials (PBFM), with particular emphasis on computational …

Nondestructive testing (NDT), a critical method for assessing the safety of ship structures,
often requires large amounts of data. The physics-informed neural network (PINN), by …

This study introduces a novel computational framework that incorporates Physics-Informed
Machine Learning (PIML) which leverages Automatic Differentiation (AD) to directly integrate …