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

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...

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Veröffentlicht in:	Computational mechanics Jg. 72; H. 3; S. 563 - 576
Hauptverfasser:	Lu, Minglei, Mohammadi, Ali, Meng, Zhaoxu, Meng, Xuhui, Li, Gang, Li, Zhen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023 Springer Springer Nature B.V
Schlagworte:	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
ISSN:	0178-7675, 1432-0924
Online-Zugang:	Volltext
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