Quantum theory, originally proposed as a physical theory to describe the motions of
microscopic particles, has been applied to various non-physics domains involving human …

The stiffness of soft biological tissues not only depends on the applied deformation, but also
on the deformation rate. To model this type of behavior, traditional approaches select a …

In the present work, a novel modeling strategy to accelerate multi-scale simulations of
heterogeneous materials using deep neural networks is developed. This approach, called …

Solving nonlinear multiscale methods with history-dependent behaviors and fine
macroscopic meshes is a well-known challenge. In this work, an unsupervised machine …

The complex behavior of inelastic woven composites stems primarily from their inherent
heterogeneity. Achieving accurate predictions of their linear and nonlinear responses, while …

During the last few decades, industries such as aerospace and wind energy (among others)
have been remarkably influenced by the introduction of high-performance composites. One …

Constitutive modelling based on machine learning (ML) approaches has surged in the last
couple of decades due to novel and more robust model architectures and computational …

Abstract Machine learning (ML) models are widely used across numerous scientific and
engineering disciplines due to their exceptional performance, flexibility, prediction quality …

Despite their recent success, machine learning (ML) models such as graph neural networks
(GNNs), suffer from drawbacks such as the need for large training datasets and poor …

Phononic metamaterials have the capability to manipulate the propagation of mechanical
waves. The traditional finite element (FE) analysis-based methods for predicting phonon …