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近期文章
Learning local, quenched disorder in plasticity and other crackling noise phenomena (机器学习用于研究塑性及其他爆裂噪声现象中的局域淬火无序)
发布时间:2018-06-21

Learning local, quenched disorder in plasticity and other crackling noise phenomena (机器学习用于研究塑性及其他爆裂噪声现象中的局域淬火无序) 
Stefanos Papanikolaou
npj Computational Materials 4:27 (2018)
doi:10.1038/s41524-018-0083-x
Published online:07 june 2018
Abstract| Full Text | PDF OPEN

摘要:多体系统在远离平衡时会表现出强烈依赖于初始条件的行为。一个典型的例子是晶态及非晶态的塑性强烈依赖于材料的历史过程。在塑性模拟中,该历史过程可以由淬火、局域和无序的流变应力决定。尽管这种无序造成了纳米尺度塑性形变时常见的雪崩,但其泛函形式及标度性却并不清楚。本研究提出了一个普适的形式,用于从爆裂噪声模拟的外场响应(如,应力/应变)时序中获得局域无序的分布。本研究采用洄滞随机-场伊辛模型和弹性界面退钉扎模型(这两种模型曾被用来模拟晶态和非晶态塑性)验证了该方法的效率。我们发现,通过提高时间分辨率和增加样品数目,可以提升模拟淬火无序分布的精确度   

Abstract:When far from equilibrium, many-body systems display behavior that strongly depends on the initial conditions. A characteristic such example is the phenomenon of plasticity of crystalline and amorphous materials that strongly depends on the material history. In plasticity modeling, the history is captured by a quenched, local and disordered flow stress distribution.While it is this disorder that causes avalanches that are commonly observed during nanoscale plastic deformation, the functional form and scaling properties have remained elusive. In this paper, a generic formalism is developed for deriving local disorder distributions from field-response (e.g., stress/strain) timeseries in models of crackling noise.We demonstrate the efficiency of the method in the hysteretic random-field Ising model and also, models of elastic interface depinning that have been used to model crystalline and amorphous plasticity. We show that the capacity to resolve the quenched disorder distribution improves with the temporal resolution and number of samples. 

Editorial Summary

Stochastic yield: machine learning predicts disorder distributions(随机屈服:机器学习预测无序分布) 

机器学习可以从爆裂应力-应变曲线中估算纳米尺度的局域无序分布,即便其带有类似共存普适行为。美国西弗吉尼亚大学的Stefanos Papanikolaou教授将无监督机器学习方法与聚类算法相结合,以期从具有爆裂噪声随时间演化行为的应力-应变曲线中得到淬火局域的无序分布。他的方法在两种爆裂噪声模型中,成功实现了数据的聚类和分类,并从镍微柱单轴压缩实验的数据中成功得到了淬火无序的分布。将这些淬火无序分布的识别及分类扩展到不同材料、加载模式和样品加载历史中,有助于建立随机屈服分布的数据库,进而改进多尺度力学模型。

Machine learning can estimate nanoscale local disorder distributions from crackling stress–strain curves, even with similar coexisting universal behavior. Stefanos Papanikolaou at The West Virginia University in West Virginia used unsupervised machine learning coupled with clustering to derive locally quenched disorder distributions from stress-strain curves that exhibit crackling noise over time.This method was successful in clustering and classifying data in two different models of crackling noise as well as in deriving the quenched disorder distribution for the experimental uniaxial compression of nickel micropillars. Extending the identification and classification of these quenched distributions to different materials, loading modes, and sample loading histories may help produce a library of stochastic yield distributions that can improve multiscale mechanics models.

 

 
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