Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

• Published in: Computational mechanics Vol. 72; no. 3; pp. 563 - 576
• Main Authors: Lu, Minglei, Mohammadi, Ali, Meng, Zhaoxu, Meng, Xuhui, Li, Gang, Li, Zhen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023; Springer; Springer Nature B.V
• Subjects: 3D printing; Accuracy; Beamforming; CAD; CAD-CAM systems; Classical and Continuum Physics; Comparative analysis; Computational Science and Engineering; Computer aided design; Computer programs; Constituents; Dynamic loads; Engineering; Finite element method; Force distribution; Gaussian beams (optics); Gaussian process; Learning; Manufacturing; Mathematics; Mechanical analysis; Mechanical properties; Mechanics; Modulus of elasticity; Original Paper; Particulate composites; Physical properties; Physics; Strain; Stress distribution; Structural design; Theoretical and Applied Mechanics; Three dimensional composites; Training; Transient response; Additive manufacturing; Neural network; Finite element analysis; Deep operator network; Machine learning

Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. Using two or more constituent materials with different physical and...