Modelling and simulation techniques are now considered an essential practice for the
materials industry. In order to gain insight into factors that can affect the final properties of a …

In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network
(HiDeNN) is proposed to solve challenging computational science and engineering …

In this paper we introduce GP+, an open-source library for kernel-based learning via
Gaussian processes (GPs) which are powerful statistical models that are completely …

Recently, deep learning methods have become one of the hottest topics in predicting
material properties, however, one bottleneck in current research is the simultaneous …

Integrated computational materials engineering (ICME) is a simulation‐driven design
approach that employs multiscale‐multiphysics modeling and is based on the …

A bottom-up multi-scale modeling approach is used to develop an Integrated Computational
Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) …

The complex spatial micro-and macrostructure layout cause an enormous difference in the
overall performance of composites. Cross-scale optimization is computationally very …

Short fiber–reinforced composites (SFRCs) are high-performance engineering materials for
lightweight structural applications in the automotive and electronics industries. Typically …

Poor interface toughening reduces the utilisation of particle/fibre-reinforced thermoset
polymer composites in many engineering applications. Numerous studies have been …

The matrix-dominated mechanical response of a unidirectional carbon/epoxy system is
studied through experiments and constitutive modeling. Experiments were performed on off …