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Recent Progress of Uncertainty Quantification in Small-Scale Materials Science
Progress in Materials Science ( IF 33.6 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.pmatsci.2020.100723
Pınar Acar

Abstract This work addresses a comprehensive review of the recent efforts for uncertainty quantification in small-scale materials science. Experimental and computational studies for analyzing and designing materials in small length-scales, such as atomistic, molecular, and meso levels, have emerged substantially over the last decade. With the advancement in computational resources, uncertainty quantification has started to garner interest in the community. The effects of uncertainties have been found to be critical in numerous studies as they lead to significant deviations on the expected material response and alter the component performance. In the field of small-scale materials science, typical resources of the uncertainties are classified as: (i) inherent material stochasticity (aleatoric uncertainty) associated with processing; (ii) modeling and algorithmic variations (epistemic uncertainty) that arise from the lack of knowledge on the systems/models. The present work reviews the recent efforts in the field and categorize according to various aspects: (i) types of uncertainties, (ii) types of uncertainty quantification problems, (iii) algorithms that are used to study the uncertainties, and (iv) length-scales in different applications. The extensive discussion covers the state-of-the-art and promising future techniques and applications, including the integration of the uncertainty quantification, design, optimization and reliability methods, and uncertainty quantification in advanced manufacturing.

中文翻译:

小规模材料科学中不确定性量化的新进展

摘要 这项工作全面回顾了最近在小规模材料科学中不确定性量化的努力。在过去十年中,用于分析和设计小长度尺度(例如原子、分子和介观水平)材料的实验和计算研究已经大量涌现。随着计算资源的进步,不确定性量化开始引起社区的兴趣。在众多研究中发现不确定性的影响至关重要,因为它们会导致预期材料响应的显着偏差并改变组件性能。在小尺度材料科学领域,不确定性的典型资源被归类为:(i)与加工相关的固有材料随机性(任意不确定性);(ii) 由于缺乏系统/模型知识而产生的建模和算法变化(认知不确定性)。目前的工作回顾了该领域最近的努力,并根据各个方面进行分类:(i)不确定性的类型,(ii)不确定性量化问题的类型,(iii)用于研究不确定性的算法,以及(iv)长度- 在不同的应用程序中缩放。广泛的讨论涵盖了最先进和有前途的未来技术和应用,包括不确定性量化、设计、优化和可靠性方法的集成,以及先进制造中的不确定性量化。目前的工作回顾了该领域最近的努力,并根据各个方面进行分类:(i)不确定性的类型,(ii)不确定性量化问题的类型,(iii)用于研究不确定性的算法,以及(iv)长度- 在不同的应用程序中缩放。广泛的讨论涵盖了最先进和有前途的未来技术和应用,包括不确定性量化、设计、优化和可靠性方法的集成,以及先进制造中的不确定性量化。目前的工作回顾了该领域最近的努力,并根据各个方面进行分类:(i)不确定性的类型,(ii)不确定性量化问题的类型,(iii)用于研究不确定性的算法,以及(iv)长度- 在不同的应用程序中缩放。广泛的讨论涵盖了最先进和有前途的未来技术和应用,包括不确定性量化、设计、优化和可靠性方法的集成,以及先进制造中的不确定性量化。
更新日期:2020-08-01
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