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相关概念视频

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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Functionalism01:11

Functionalism

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William James, John Dewey, and Charles Sanders Peirce were instrumental in founding functional psychology, which draws heavily from Darwin's theory of evolution by natural selection. This theory suggests that individual traits, including behaviors, are adapted to their environments through natural selection. At the heart of functionalism is the concept of adaptation, meaning that a trait enhances an individual's chances of survival and reproduction.
James envisioned psychology's...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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用简单的功能形式进行数据驱动粗粒化.

Dylan D Fortney1, Brett M Savoie2

  • 1Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States.

Journal of chemical theory and computation
|August 5, 2025
PubMed
概括

用粗粒度分子动力学 (CGMD) 的遗传算法优化简单的莱纳德-斯潜力,成功地重现了复杂的材料特性. 这种数据驱动的方法在CGMD模拟中比复杂的神经网络提供了优势.

科学领域:

  • 计算材料科学 计算材料科学
  • 分子动力学模拟的模拟.
  • 机器学习在化学中的应用

背景情况:

  • 深度神经网络对于粗粒度分子动力学 (CGMD) 潜能很受欢迎,因为它们的复杂性和训练容易.
  • 传统的功能形式更简单,但可能缺乏对复杂系统的描述能力.

研究的目的:

  • 调查CGMD更简单的功能形式的数据驱动优化对CGMD的潜在优势.
  • 开发和评估一个优化莱纳德-斯潜力的遗传算法.

主要方法:

  • 开发了一种遗传算法,以优化CGMD模型的伦纳德-斯潜力.
  • 优化基于晶体和液晶材料的结构和热力学数据.
  • 提供了算法,损失函数和超参数的详细描述.

主要成果:

  • 优化的模型比更简单的参数化方案再现了更广泛的物理性质.
  • 模型显示出令人惊的可转移性,预测了培训中不包括的属性.
  • 模拟显示了晶体结构的稳定,保存了点趋势,并重现了液晶相位过渡.

结论:

  • 更简单的功能形式,当与基因算法等数据驱动的训练算法相结合时,保留了CGMD的重大未开发潜力.

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  • 这种方法为某些CGMD应用提供了复杂神经网络潜力的可行和有效的替代方案.