Issue 1/2023
Special Issue: Machine Learning Theories, Modeling, and Applications to Computational Materials Science, Additive Manufacturing, Mechanics of Materials, Design and Optimization, Volume I : Multiscale Mechanics of Materials / Guest Edited by Miguel Bessa, Shaofan Li, Chinesta Francisco
Content (12 Articles)
Editorial
Special issue of computational mechanics on machine learning theories, modeling, and applications to computational materials science, additive manufacturing, mechanics of materials, design and optimization
Wing Kam Liu, Miguel A. Bessa, Francisco Chinesta, Shaofan Li, Nathaniel Trask
Original Paper
Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis
Shiguang Deng, Diran Apelian, Ramin Bostanabad
Original Paper
Reducing internal variables and improving efficiency in data-driven modelling of anisotropic damage from RVE simulations
Julien Yvonnet, Qi-Chang He, Pengfei Li
Original Paper
Deep learning and multi-level featurization of graph representations of microstructural data
Reese Jones, Cosmin Safta, Ari Frankel
Original Paper
A machine-learning aided multiscale homogenization model for crystal plasticity: application for face-centered cubic single crystals
Dana Bishara, Shaofan Li
Original Paper
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Ruben Villarreal, Nikolaos N. Vlassis, Nhon N. Phan, Tommie A. Catanach, Reese E. Jones, Nathaniel A. Trask, Sharlotte L. B. Kramer, WaiChing Sun
Open Access
Original Paper
Physics-informed machine-learning model of temperature evolution under solid phase processes
Ethan King, Yulan Li, Shenyang Hu, Eric Machorro
Original Paper
A Bayesian regularization network approach to thermal distortion control in 3D printing
Yuxi Xie, Boyuan Li, Chao Wang, Kun Zhou, C. T. Wu, Shaofan Li
Open Access
Original Paper
Efficient multiscale modeling of heterogeneous materials using deep neural networks
Fadi Aldakheel, Elsayed S. Elsayed, Tarek I. Zohdi, Peter Wriggers
Original Paper
HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis
Yingjian Liu, Chanwook Park, Ye Lu, Satyajit Mojumder, Wing Kam Liu, Dong Qian
Original Paper
An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis
Owen Huang, Sourav Saha, Jiachen Guo, Wing Kam Liu
Original Paper
Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials
Zihan Wang, Weikang Xian, Ying Li, Hongyi Xu