Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of
improving patient outcomes and optimizing healthcare delivery. By harnessing machine …

The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …

In this work, we present a Parametric Deep Energy Method (P-DEM) for elasticity problems
accounting for strain gradient effects. The approach is based on physics-informed neural …

In this paper, we present a deep autoencoder based energy method (DAEM) for the
bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher …

Phase field models, in particular, the Allen-Cahn type and Cahn-Hilliard type equations,
have been widely used to investigate interfacial dynamic problems. Designing accurate …

Solving general high-dimensional partial differential equations (PDE) is a long-standing
challenge in numerical mathematics. In this paper, we propose a novel approach to solve …

This paper formulates a model order reduction method for electromagnetic boundary
element analysis and extends it to computer-aided design integrated shape optimization of …

This article tries to investigate the dynamic deflection response of exponentially functionally
graded material (E-FGM) nanoplate considering the role of porosities when embedded in a …

Abstract We present a Physics-Informed Neural Network (PINN) to simulate the
thermochemical evolution of a composite material on a tool undergoing cure in an …

Abstract Physics-Informed Neural Networks (PINNs) have recently gained increasing
attention in the field of topology optimization. The fusion of deep learning and topology …