Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …

Today's manufacturing processes are pushed to their limits to generate products with ever-
increasing quality at low costs. A prominent hurdle on this path arises from the multiscale …

Machine learning (ML) has been perceived as a promising tool for the design and discovery
of novel materials for a broad range of applications. In this prospective paper, we summarize …

Current composite design processes go through expensive numerical simulations that can
quantitatively describe the detailed complex stress state embedded in the laminate structure …

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional
objects from a computer-aided design model by successively adding material layer by layer …

Polycrystalline titanium alloys exhibit anisotropic elastic and plastic properties, which hinder
their extensive application as structural components. Overcoming this anisotropy via texture …

In this work, a physics-informed neural network (PINN) designed specifically for analyzing
digital materials is introduced. This proposed machine learning (ML) model can be trained …

In recent years, the development of artificial intelligence (AI) and machine learning (ML)
techniques has revolutionized composite design. Researchers have investigated intricate …

Determining process–structure–property linkages is one of the key objectives in material
science, and uncertainty quantification plays a critical role in understanding both process …

Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …