The glass science and technology is a rapidly developing field which is focused on
development of new glasses with excellent properties. Glasses are the non-crystalline …

Developing accurate yet fast computational tools to simulate complex physical phenomena
is a long-standing problem. Recent advances in machine learning have revolutionized the …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

Using deep learning to analyze mechanical stress distributions is gaining interest with the
demand for fast stress analysis. Deep learning approaches have achieved excellent …

We present a new Integrated Finite Element Neural Network framework (I-FENN), with the
objective to accelerate the numerical solution of nonlinear computational mechanics …

Multiscale computational modelling is challenging due to the high computational cost of
direct numerical simulation by finite elements. To address this issue, concurrent multiscale …

This article presents the concepts, methodologies, and applications of In-Process Quality
Improvement (IPQI) in complex manufacturing systems. As opposed to traditional quality …

Architected materials typically rely on regular periodic patterns to achieve improved
mechanical properties such as stiffness or fracture toughness. Here we introduce a class of …

Extrusion-based 3D concrete printing (3DCP) results in deposited materials with complex
microstructures that have high porosity and distinct anisotropy. Due to the material …

A machine learning model is proposed to predict the brittle fracture of polycrystalline
graphene under tensile loading. The model employs a convolutional neural network …