For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Classical numerical methods such as finite element method (FEM) face limitations due to
their low efficiency when addressing large-scale problems. As a novel paradigm, the physics …

Data-driven methods have recently been introduced to address complex mechanics
problems. While model-based, data-driven approaches are predominantly used, they often …

In our recently proposed Integrated Finite Element Neural Network (I-FENN) framework
(Pantidis and Mobasher, 2023) we showcased how PINNs can be deployed on a finite …

Abstract Machine learning (ML) has been used to solve multiphysics problems like
thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high …

Biological structures optimized through natural selection provide valuable insights for
engineering load-bearing components. This paper reviews six key strategies evolved in …

The physics-informed neural network (PINN) has received much attention in the field of
partial differential equation (PDE) solving due to its adaptability to different governing …

Inverse analysis of material parameters and load conditions are crucial in boundary value
problems, which are always challenging and time-consuming for existing numerical …

In this work, we propose a deep learning (DL)-based constitutive model for investigating the
cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites …