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相关概念视频

Diffusion01:21

Diffusion

6.2K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.2K
Diffusion01:12

Diffusion

215.8K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
215.8K
Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

1.2K
Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
1.2K
Reynolds Transport Theorem01:24

Reynolds Transport Theorem

1.8K
The Reynolds transport theorem provides a framework to relate the time rate of change of an extensive property within a system to that in a control volume, which is crucial for analyzing fluid dynamics. Extensive properties, such as mass, velocity, acceleration, temperature, and momentum, can be expressed in terms of the mass of a fluid portion. These properties are called extensive because they depend on the system's size, while intensive properties are their corresponding values per unit...
1.8K
Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

31.1K
Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
31.1K
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

322
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
322

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相关实验视频

Updated: Jan 11, 2026

The Diffusion of Passive Tracers in Laminar Shear Flow
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The Diffusion of Passive Tracers in Laminar Shear Flow

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为了扩散而扩散:在污染物运输中,一个多功能多物理领域的精细化框架.

Yinpeng Wang1, Yuancheng Zhan2

  • 1National University of Singapore, Singapore.

Water research
|November 16, 2025
PubMed
概括

一个新的扩散模型深度学习框架从有限的数据准确地重建了污染物运输领域. 这种方法通过提高预测的准确性和效率来加强环境风险评估和工业污染减轻.

科学领域:

  • 环境科学 环境科学
  • 计算流体动力学的流体动力学.
  • 机器学习 机器学习

背景情况:

  • 准确的污染物运输建模对于环境风险评估和工业污染控制至关重要.
  • 当前的数值解决器和机器学习方法在计算效率,概括性和预测准确性方面面临挑战.

研究的目的:

  • 开发一种新的深度学习框架,以通过稀疏的测量来改进污染物运输中的合多物理场.
  • 解决现有方法在效率,通用性和准确性方面的局限性.

主要方法:

  • 一个以物理学为基础的,以方程为约束的深度学习框架,基于条件扩散模型.
  • 在2D过渡和3D稳定状态场景中,通过合的度和温度梯度驱动的污染物扩散的应用.
  • 使用来自有限元模拟和双色警报成像的基准数据集进行验证.

主要成果:

  • 拟议的扩散模型框架从有限的观测中准确地恢复了高维的时空污染物场.
  • 在超分辨率准确性和稳定性方面,始终优于最先进的基线 (ResNet,变压器,富里埃神经运算器).
  • 在各种源分布和边界条件中证明有效性.

结论:

  • 开发的扩散模型深度学习方法在污染物运输领域的重建方面取得了重大进展.
关键词:
扩散模型的扩散模型.多物理领域的精炼.污染物的运输污染物运输.稀少的数据增强.

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  • 这种方法为环境和工业污染环境中的实时监测和分析提供了有希望的途径.
  • 该框架显示了改善环境风险评估和污染减缓战略的潜力.