The determination of mechanical properties plays a crucial role in utilizing composite
materials across multiple engineering disciplines. Recently, there has been substantial …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

We introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-
tuned properties. Denoising diffusion probabilistic models are generative models that use …

This paper proposes a microstructure reconstruction framework with denoising diffusion
models for the first time. The novelty and strength of the proposed model lie in its universality …

In the present work, 3D convolutional neural networks (CNNs) are trained to link random
heterogeneous, multiphase materials to their elastic macroscale stiffness thus replacing …

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

Microstructure reconstruction, a major component of inverse computational materials
engineering, is currently advancing at an unprecedented rate. While various training-based …

Realistic microscale domains are an essential step towards making modern multiscale
simulations more applicable to computational materials engineering. For this purpose, 3D …

In the present report some basic issues of and some of the modeling strategies used for
studying static and quasistatic problems in continuum micromechanics of materials are …

Microstructure reconstruction is an important and emerging field of research and an
essential foundation to improving inverse computational materials engineering (ICME) …