Machine learning tools represent key enablers for empowering material scientists and
engineers to accelerate the development of novel materials, processes and techniques. One …

Data-driven models are rising as an auspicious method for the geometrical design of
materials and structural systems. Nevertheless, existing data-driven models customarily …

With an emphasis on the development of machine learning-based constitutive modeling
approaches, the state of constitutive modeling techniques and applications for metals and …

Predicting history-dependent materials' responses is crucial, as path-dependent behavior
appears while characterizing or geometrically designing many materials (eg, metallic and …

Deep learning (DL) and the collocation method are merged and used to solve partial
differential equations (PDEs) describing structures' deformation. We have considered …

In this work, we investigate the performance of data-driven modeling for mechanical property
prediction of a simulated microstructural dataset. The dataset comprises realistic …

Abstract Machine learning (ML) is shown to predict new alloys and their performances in a
high dimensional, multiple-target-property design space that considers chemistry, multi-step …

The potential energy formulation and deep learning are merged to solve partial differential
equations governing the deformation in hyperelastic and viscoelastic materials. The …

Physics‐informed neural networks have gained growing interest. Specifically, they are used
to solve partial differential equations governing several physical phenomena. However …

Material identification is critical for understanding the relationship between mechanical
properties and the associated mechanical functions. However, material identification is a …