A k‐means clustering machine learning‐based multiscale method for anelastic heterogeneous structures with internal variables

• Published in: International journal for numerical methods in engineering Vol. 123; no. 9; pp. 2012 - 2041
• Main Authors: Benaimeche, Mohamed Amine, Yvonnet, Julien, Bary, Benoit, He, Qi‐Chang
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.05.2022; Wiley Subscription Services, Inc; Wiley
• Subjects: Anelasticity; Clustering; Dependent variables; Elastoplasticity; Engineering Sciences; FE2; Finite element method; homogenization; k‐means clustering; Load history; Machine learning; Mechanical properties; multiscale; Multiscale analysis; nonlinear; Radioactive wastes; Homogenization; FE2; k-means clustering; Nonlinear; Multiscale; Machine learning

A new machine‐learning based multiscale method, called k‐means FE2, is introduced to solve general nonlinear multiscale problems with internal variables and loading history‐dependent behaviors, without use of surrogate models. The macro scale problem is reduced by constructing clusters of Gauss poin...