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相关概念视频

Phase Transitions02:31

Phase Transitions

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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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First Order Systems01:21

First Order Systems

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First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
129
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Phase Transitions: Vaporization and Condensation02:39

Phase Transitions: Vaporization and Condensation

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The physical form of a substance changes on changing its temperature. For example, raising the temperature of a liquid causes the liquid to vaporize (convert into vapor). The process is called vaporization—a surface phenomenon. Vaporization occurs when the thermal motion of the molecules overcome the intermolecular forces, and the molecules (at the surface) escape into the gaseous state. When a liquid vaporizes in a closed container, gas molecules cannot escape. As these gas phase...
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Phase Transitions: Sublimation and Deposition02:33

Phase Transitions: Sublimation and Deposition

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Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...
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Transfer Function to State Space01:23

Transfer Function to State Space

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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模拟与等级自回归网络的第一阶段过渡.

Piotr Białas1, Paulina Czarnota2, Piotr Korcyl3

  • 1Institute of Applied Computer Science, Jagiellonian University, 30-348 Kraków, Poland.

Physical review. E
|June 17, 2023
PubMed
概括

一个新的等级自回归神经网络采样算法显著改善了对二维Q状态波茨模型近相过渡的模拟中的统计不确定性. 预训练可以提高大神经网络的效率.

科学领域:

  • 统计力学就是统计力学.
  • 计算物理学的计算物理.
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 统计模型中的相位过渡对于理解材料特性至关重要.
  • 像沃尔夫集群算法这样的传统算法在临界点附近的效率面临挑战.
  • 神经网络方法为改进的模拟技术提供了潜力.

研究的目的:

  • 介绍和评估2D Q-state Potts模型的层次自回归神经网络采样算法.
  • 为了将其性能与沃尔夫集群算法在第一阶段过渡附近进行比较.
  • 为了证明大神经网络训练预训练的有效性.

主要方法:

  • 应用一个层次性的自回归神经网络采样算法.
  • 在Q=12.12时,围绕2DQ状态波茨模型相位过渡进行的模拟.
  • 介绍和使用一种预训练技术来提高神经网络的效率.
  • 与沃尔夫集群算法进行比较.

主要成果:

  • 与沃尔夫集群算法相比,在类似的计算成本下,统计不确定性的显著改善.
  • 预训练技术在训练大神经网络方面表现出有效性.
  • 准确估计相位过渡附近的自由能量和.

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结论:

  • 层次自回归神经网络方法为模拟具有双模分布的系统提供了卓越的性能,特别是在相位过渡附近.
  • 在这种情况下,预训练是有效训练大神经网络的可行策略.
  • 该方法为热力学量提供了非常精确的估计,如自由能量和.