An approach is proposed to accelerate multiscale simulations of heterogeneous quasi-brittle
materials exhibiting an anisotropic damage response. The present technique uses …

In the present work, a novel modeling strategy to accelerate multi-scale simulations of
heterogeneous materials using deep neural networks is developed. This approach, called …

The article provides a detailed review of the utilisation of machine learning (ML) in various
domains of additive manufacturing (AM) and highlights its potential to address key …

Abstract Machine learning (ML) models are widely used across numerous scientific and
engineering disciplines due to their exceptional performance, flexibility, prediction quality …

An efficient data-driven multiscale framework for modeling anisotropic damage (M-DDHAD)
in heterogeneous structures is proposed, where the anisotropic damage model at the macro …

In this work, we address operator learning for stochastic homogenization in nonlinear
elasticity. A Fourier neural operator is employed to learn the map between the input field …

We propose an overlapping algorithm utilizing the K-means clustering technique to group
scattered data nodes for discretizing elliptic partial differential equations. Unlike …

The use of machine learning and deep learning has become prominent within various fields
of bioprocessing for countless modeling and prediction tasks. Previous reviews have …

This study aims to develop a multiscale bridging method for investigating nanocrystalline
metals based on macro-scale deformation. For this purpose, we propose a hierarchical …

This study explored the clustering based on arithmetic optimization algorithm (CAOA) and its
potential for addressing challenging clustering problems. CAOA is based on the arithmetic …