相变
概率逻辑
哈密顿量(控制论)
背景(考古学)
统计物理学
伊辛模型
材料科学
纳米技术
计算机科学
凝聚态物理
物理
数学
人工智能
数学优化
古生物学
生物
作者
Zekeriya Ender Eğer,Pınar Acar
摘要
This Perspective article aims to emphasize the crucial role of uncertainty quantification (UQ) in understanding magnetic phase transitions, which are pivotal in various applications, especially in the transportation and energy sectors [D. C. Jiles, Acta Mater. 51, 5907–5939 (2003) and Gutfleisch et al., Adv. Mater. 23, 821–842 (2011)]. Magnetic materials undergoing phase transitions, particularly due to high temperatures, pose challenges related to the loss of their inherent properties. However, pinpointing a definitive phase transition temperature proves challenging due to the diverse and uncertain nanostructure of materials. Deterministic approaches are limited when seeking a precise threshold. As a result, there is a need to develop probabilistic methods to improve the understanding of this physical problem. In this study, UQ is explored within the context of magnetic phase transitions. In addition, the broader applications of UQ in relation to microstructures and Hamiltonian systems are discussed to highlight its significance in materials science. Furthermore, this study discusses the potential future work on the integration of quantum computing to achieve more efficient UQ solutions of magnetic phase transitions using Ising models.
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