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Coupling in situ experiments and modeling – Opportunities for data fusion, machine learning, and discovery of emergent behavior
Current Opinion in Solid State & Materials Science ( IF 11.0 ) Pub Date : 2019-11-30 , DOI: 10.1016/j.cossms.2019.100797
Michael D. Sangid

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.



中文翻译:

耦合原位实验和建模–数据融合,机器学习和紧急行为发现的机会

本文回顾了最近的研究,该研究不仅包括实验和建模组件,而且还赞扬了这些技术之间的紧密结合,以提供对多晶金属的可塑性和破坏性的见解。提供了多尺度研究的实例,包括但不限于与原子探针层析成像相结合的密度泛函理论,与原位透射电子错误复制相结合的分子动力学,与纳米柱实验相结合的离散位错动力学,与数字图像相结合的晶体可塑性相关性,以及晶体可塑性与原位高能X射线衍射相结合。通过提供直接的比较手段,原位实验与建模之间的紧密协作为模型校准,验证和确认提供了新的机会,从而消除了方法中认知不确定性的各个方面。此外,原位实验数据和基于模型的数据之间的数据融合以及数据驱动方法为确定变形和破坏的紧急行为提供了范式转变,这是支撑多晶材料机械行为的基础。

更新日期:2019-11-30
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