A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials

Multiscale simulation and homogenization of materials have become the major computational technology as well as engineering tools in material modeling and material design. However, the concurrent multiscale simulations require extensive computational resources, in which the CPU time increases expone...

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Vydané v:Archives of computational methods in engineering Ročník 30; číslo 1; s. 191 - 222
Hlavní autori: Bishara, Dana, Xie, Yuxi, Liu, Wing Kam, Li, Shaofan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.01.2023
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Artificial intelligence
Computational chemistry
Computational efficiency
Computational mechanics
Computational physics
Computing costs
Decision making
Decision trees
Engineering
Finite element analysis
Homogenization
Machine learning
Mathematical and Computational Engineering
Mechanics
Modelling
Numerical analysis
Partial differential equations
Review Article
Simulation
State-of-the-art reviews
Support vector machines
ISSN:1134-3060, 1886-1784
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Shrnutí:Multiscale simulation and homogenization of materials have become the major computational technology as well as engineering tools in material modeling and material design. However, the concurrent multiscale simulations require extensive computational resources, in which the CPU time increases exponentially as the spatial and temporal scale increase. In fact, with only a few exceptions, both hierarchical and concurrent multiscale modeling techniques have not been adopted in the industrial sector, primarily because of their computational cost. Recently, the rapid developments in artificial intelligence technology as well as the fast growth in computational resources and data have stimulated a widespread adoption of machine learning-based methodologies to enhance the computational efficiency and accuracy in multiscale simulations and their applications. Even though there is a great expectation of a revolution propelled by the artificial intelligence and machine learning technology in computational materials and computational mechanics, the machine learning-based multiscale modeling and simulation is still at its infant stage. In this paper, we aim at a state-of-the-art review on the machine learning-based multiscale modeling and simulation of materials, and its applications in composite homogenization, defect mechanics modeling, and material design, to provide an overview as well as perspectives on these innovative techniques, which may soon replace the conventional multiscale modeling methods.
Bibliografia:ObjectType-Article-1
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ISSN:1134-3060
1886-1784
DOI:10.1007/s11831-022-09795-8