A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua

Xiao, Shaoping; Hu, Renjie; Li, Zhen; Attarian, Siamak; Björk, Kaj‐Mikael; Lendasse, Amaury

doi:10.1007/s00521-019-04480-7

Cited by 21 publications

(7 citation statements)

References 70 publications

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“…It has also been applied to parameter passing [36,13,46] and to the inversion of experimental data [16,11]. Somewhat closer to our work, image classification and natural language processing have been applied to approximate material constitutive behavior [38,28] and homogenization of material behavior [34,48].…”

mentioning

confidence: 88%

A learning-based multiscale method and its application to inelastic impact problems

Liu

Kovachki

et al. 2022

Journal of the Mechanics and Physics of Solids

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mentioning

confidence: 88%

A learning-based multiscale method and its application to inelastic impact problems

Liu

Kovachki

et al. 2022

Journal of the Mechanics and Physics of Solids

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“…It has also been applied to parameter passing [61,20,93] and to the inversion of experimental data [26,18]. Image classification and natural language processing have been applied to approximate material constitutive behavior [71,39] and homogenization of material behavior [59,95].…”

Section: Machine-learning Materials Behaviormentioning

confidence: 99%

Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization

Kovachki

Liu

Sun

et al. 2022

Mechanics of Materials

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The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications. The performance required for a particular application leads to the choice of a particular material system whose properties are optimized by manipulating its underlying microstructure through processing. The specific configuration of the structure is then designed by characterizing the material in detail, and using this characterization along with physical principles in system level simulations and optimization. These have been advanced by multiscale modeling of materials, high-throughput experimentations, materials data-bases, topology optimization and other ideas. Still, developing materials for extreme applications involving large deformation, high strain rates and high temperatures remains a challenge. This article reviews a number of recent methods that advance the goal of designing materials targeted by specific applications.

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“…Multiscale approaches are particularly attractive in the CNTs-based composite structure due to atomic scale dependencies of a single CNT [4]. Multiscale methods have been mainly categorized into two classes: hierarchical and concurrent multiscale methods [20]. In hierarchical approaches, the molecular and macro models are simulated sequentially.…”

Section: Introductionmentioning

confidence: 99%

A machine-learning-enabled approach for bridging multiscale simulations of CNTs/PDMS composites

Yu,

Zhi,

Sun

et al. 2024

NSO

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Benefitting from the interlaced networking structure of carbon nanotubes (CNTs), the composites of CNTs/polydimethylsiloxane (PDMS) have found extensive applications in wearable electronics. While hierarchical multiscale simulation frameworks exist to optimize the structure parameters, their wide applications were hindered by the high computational cost. In this study, a machine learning model based on the artificial neural networks (ANN) embedded graph attention network, termed as AGAT, was proposed. The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model. The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale, while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites. By comparing the AGAT model with the original multiscale simulation results, the data-driven strategy is shown to be promising with high accuracy, demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.

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A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua

Cited by 21 publications

References 70 publications

A learning-based multiscale method and its application to inelastic impact problems

A learning-based multiscale method and its application to inelastic impact problems

Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization

A machine-learning-enabled approach for bridging multiscale simulations of CNTs/PDMS composites

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