Tung-Huan Su, Szu-Jui Huang, J. Jean, Chuin-Shan Chen
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Multiscale computational solid mechanics: data and machine learning
Multiscale computational solid mechanics concurrently connects complex material physics and macroscopic structural analysis to accelerate the application of advanced materials in the industry rather than resorting to empirical constitutive models. The rise of data-driven multiscale material modeling opens a major paradigm shift in multiscale computational solid mechanics in the era of material big data. This paper reviews state-of-the-art data-driven methods for multiscale simulation, focusing on data-driven multiscale finite element method (data-driven FE2) and data-driven multiscale finite element-deep material network method (data-driven FE-DMN). Both types of data-driven multiscale methods aim to resolve the past challenge of concurrent multiscale simulation. Numerical examples are designed to demonstrate the effectiveness of data-driven multiscale simulation methods. Future research directions are discussed, including data sampling strategy and data generation technique for the data-driven FE2 method and generalization of data-driven FE-DMN method.
期刊介绍:
The objective of the Journal of Mechanics is to provide an international forum to foster exchange of ideas among mechanics communities in different parts of world. The Journal of Mechanics publishes original research in all fields of theoretical and applied mechanics. The Journal especially welcomes papers that are related to recent technological advances. The contributions, which may be analytical, experimental or numerical, should be of significance to the progress of mechanics. Papers which are merely illustrations of established principles and procedures will generally not be accepted. Reports that are of technical interest are published as short articles. Review articles are published only by invitation.