As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

Literature sources have been reviewed, various techniques, methods and software for
investigating fracture of polycrystalline materials have been subjected to the systematic …

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 …

In the mass production of electronic products, in-circuit-test (ICT) and printed circuit board
assembly (PCBA) quality tests are performed. ICT measures resistance values and …

High temperature design requires accurate constitutive models to describe material inelastic
deformation and failure behavior. Oftentimes, calibrating accurate models devolves into the …

Colloidal material design necessitates a collection of computer approaches ranging from
quantum chemistry to molecular dynamics and continuum modeling. Machine learning (ML) …

High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-
based models can become computationally demanding as the number of microcracks …

Nuclear energy is presently the single major low-carbon electricity source in Europe and is
overall expected to maintain (perhaps eventually even increase) its current installed power …

Deep learning has been widely used in different fields such as computer vision and speech
processing. The performance of deep learning algorithms is greatly affected by their …

Recent progress combining micromechanics and machine learning holds promise for
accurately and rapidly predicting the mechanical behavior of complex composite materials …