Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Topology optimization (TO) is a method of deriving an optimal design that satisfies a given
load and boundary conditions within a design domain. This method enables effective design …

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 …

Physics‐informed neural networks (PINNs) are a class of deep neural networks that are
trained, using automatic differentiation, to compute the response of systems governed by …

In this paper, we introduce a new approach based on distance fields to exactly impose
boundary conditions in physics-informed deep neural networks. The challenges in satisfying …

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 …

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 …

Despite its rapid development, Physics-Informed Neural Network (PINN)-based
computational solid mechanics is still in its infancy. In PINN, the loss function plays a critical …

This work studies the size-dependent stability analysis of restrained nanobeam with
functionally graded material via nonlocal Euler–Bernoulli beam theory using the Fourier …

This paper presents an effective method for crack identification to improve the training of
Artificial Neural Networks (ANN) parameters using Jaya algorithm. Dynamic and static …