Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary
scientific domains where machines are provided with an approximation of human …

Mechanical metamaterials with unique micro-architectures possess excellent physical
properties in terms of stiffness, toughness, vibration isolation, and thermal expansion …

Feedforward backpropagation artificial neural networks (ANNs) have been increasingly
employed in many engineering practices concerning materials modeling. Despite their …

This paper investigates the accuracy of the existing empirical design models and different
machine learning (ML) models, known as Decision Tree (DT), Random Forest (RF), K …

Dramatic advancements in interdisciplinary research with the fourth paradigm of science,
especially the implementation of computer science, nourish the potential for artificial …

Multiscale computing for heterogeneous materials provides a powerful and fidelity approach
to handling situations where a suitable macroscopic constitutive model is unavailable …

Abstract Machine learning (ML) and deep learning (DL) approaches can solve the same
problems as the finite element method (FEM) with a high degree of accuracy in a fraction of …

In a real-life environment, construction machinery and other large equipment are subjected
to dynamic loads that cause structural fatigue or failure. Therefore, it is necessary to assess …

Vertical ground reaction force (GRFz) measurements are the best tool for assessing horses'
weight-bearing lameness. However, collection of these data is often impractical for clinical …

Traditional model-based control methods require accurate system dynamics. However, the
dynamics are usually unknown and it is challenging to tune the control parameters manually …