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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Introduction to Structures01:30

Introduction to Structures

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A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
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Humans have been making ships, shelters, pyramids, weapons, agricultural equipment, and many more items without recording the process or theory behind them for centuries. It would be challenging to document the evolution of mechanics from its origin to the present.
According to records, the history of mechanics starts with Aristotle (384–322 BC). He related mechanics to physical theory, aiming for a universal synthesis.
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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Physics is concerned with the interactions of energy, matter, space, and time, in order to discover the underlying mechanisms that underpin all phenomena. The word "physics" comes from the Greek word "phúsis", which means nature. Physics seeks to comprehend the natural world around us at its most fundamental level. It emphasizes the use of quantitative laws to do this, which could be valuable in other fields that want to push the performance boundaries of present...
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主题号"基于物理的机器学习及其结构完整性应用"的序言.

Shun-Peng Zhu1, Abílio M P De Jesus2, Filippo Berto3

  • 1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|September 24, 2023
PubMed
概括

基于物理的机器学习通过将物理定律集成到模型中来增强结构完整性的评估. 这种方法提高了工程系统的概括性和可靠性,推进了材料科学和安全评估.

关键词:
失效机制建模失效机制建模机器学习是机器学习.基于物理的机器学习.预后和健康管理.结构完整性的结构完整性.

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科学领域:

  • 工程 工程师 工程师 工程师
  • 数据科学数据科学数据科学
  • 物理 物理学 物理

背景情况:

  • 机器学习 (ML) 在工程方面提供了巨大的潜力,但缺乏物理意义和通用性.
  • 纯粹数据驱动的模型与新的场景和物理解释性作斗争.
  • 将物理整合到机器学习中对于强大的工程应用至关重要.

研究的目的:

  • 提供对基于物理的机器学习 (PIML) 的最新审查.
  • 突出PIML在结构完整性和安全性评估中的应用.
  • 探索材料科学中实时数据分析的高级ML算法.

主要方法:

  • 将物理原理纳入机器学习算法.
  • 开发复杂的ML技术用于数据分析.
  • 专注于材料科学,疲劳和断裂力学.

主要成果:

  • 皮姆 (PIML) 模型表现出更好的概括性和物理解释性.
  • 在实时数据处理中提高准确性和生产力.
  • 设计具有可靠安全评估的新材料和结构的潜力.

结论:

  • 基于物理的机器学习是工程学的转型领域.
  • PIML提高了ML在结构完整性的可靠性和适用性.
  • 这项研究为未来材料设计和安全方面的进步铺平了道路.